MyLibrary.bib

@article{wang_quantitative_2017,
	title = {Quantitative interferometric microscopy with two dimensional Hilbert transform based phase retrieval method},
	volume = {383},
	issn = {00304018},
	doi = {10.1016/j.optcom.2016.10.008},
	abstract = {In order to obtain high contrast images and detailed descriptions of label free samples, quantitative interferometric microscopy combining with phase retrieval is designed to obtain sample phase distributions from fringes. As accuracy and efficiency of recovered phases are affected by phase retrieval methods, thus approaches owning higher precision and faster processing speed are still in demand. Here, two dimensional Hilbert transform based phase retrieval method is adopted in cellular phase imaging, it not only reserves more sample specifics compared to classical fast Fourier transform based method, but also overcomes disadvantages of traditional algorithm according to Hilbert transform which is a one dimensional processing causing phase ambiguities. Both simulations and experiments are provided, proving the proposed phase retrieval approach can acquire quantitative sample phases with high accuracy and fast speed.},
	pages = {537--544},
	journaltitle = {Optics Communications},
	author = {Wang, Shouyu and Yan, Keding and Xue, Liang},
	date = {2017-01-15},
	note = {Publisher: Elsevier B.V.},
	keywords = {Phase retrieval, Quantitative interferometric microscopy, Two dimensional Hilbert transform},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\HSU2RL97\\1-s2.0-S0030401816308707-main.pdf:application/pdf},
}

@article{darahanau_application_2005,
	title = {Application of quantitative X-ray phase retrieval from Fraunhofer diffraction data to nano-resolution profiling of materials},
	volume = {251},
	issn = {00304018},
	doi = {10.1016/j.optcom.2005.02.071},
	abstract = {X-ray Fraunhofer diffraction data have been collected from a series of weakly thickness-modulated samples using a synchrotron source. Three different areas of a linear Fresnel zone structure etched in a silicon wafer each 10 μm wide were studied. Each area studied included different numbers of zones, ranging from 6 to 20. An X-ray phase retrieval technique was used to profile the complex refractive index within the areas examined. The zone structure thickness profiles were mapped with a spatial resolution of 100 nm. © 2005 Elsevier B.V. All rights reserved.},
	pages = {100--108},
	number = {1},
	journaltitle = {Optics Communications},
	author = {Darahanau, A. V. and Nikulin, A. Y. and Souvorov, A. and Nishino, Y. and Muddle, B. C. and Ishikawa, T.},
	date = {2005-07-01},
	keywords = {Diffraction, Phase retrieval, X-rays},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\EXFZF2KR\\1-s2.0-S0030401805002002-main.pdf:application/pdf},
}

@article{nikulin_phase-retrieval_2008,
	title = {Phase-retrieval in hard x-ray diffraction imaging: Can we quantitatively reconstruct a "real" transmission function?},
	volume = {372},
	issn = {03759601},
	doi = {10.1016/j.physleta.2008.03.044},
	abstract = {X-ray phase-retrieval algorithms are widely exploited in contemporary hard x-ray diffraction techniques to image at the nanoscale, less than 10-20 nm. Often reconstruction of the sample shape (image) suffices for the purpose of experiment. Identification of specimen composition requires a quantitative profiling of the complex refractive index. This Letter shows that, although such quantitative analysis is possible in many cases, there is a lower limit to variations in optical density, which can be quantitatively reconstructed using the common phase-retrieval methods. © 2008 Elsevier B.V. All rights reserved.},
	pages = {4333--4336},
	number = {23},
	journaltitle = {Physics Letters, Section A: General, Atomic and Solid State Physics},
	author = {Nikulin, A. Y. and Dilanian, R. A. and Darahanau, A. V.},
	date = {2008-06-02},
	note = {Publisher: Elsevier},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\Z4FKIVCP\\1-s2.0-S0375960108005033-main.pdf:application/pdf},
}

@report{nakajima_phase_nodate,
	title = {Phase Retrieval Using the Properties of Entire Functions},
	author = {Nakajima, N},
	note = {Publication Title: {ADVANCES} {IN} {IMAGING} {AND} {ELECTRON} {PHYSICS}
Volume: 93},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9NN322ZM\\1-s2.0-S1076567008701344-main.pdf:application/pdf},
}

@inproceedings{zenkova_new_2020,
	title = {New simulation approach based on Hilbert transform for restoring the amplitude and phase distributions of random fields: carbon nanoparticles using},
	isbn = {978-1-5106-3510-4},
	doi = {10.1117/12.2553220},
	pages = {18},
	publisher = {{SPIE}-Intl Soc Optical Eng},
	author = {Zenkova, C.Yu. and Ryabyi, P.A. and Ivanskyi, Dmytro I. and Tkachuk, V.M. and Yan, Wenjun},
	date = {2020-02-06},
	note = {{ISSN}: 1996756X},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\XGMDQEBN\\1136905.pdf:application/pdf},
}

@article{gugel_pr-dad_2022,
	title = {{PR}-{DAD}: Phase Retrieval Using Deep Auto-Decoders},
	url = {http://arxiv.org/abs/2204.09051},
	abstract = {Phase retrieval is a well known ill-posed inverse problem where one tries to recover images given only the magnitude values of their Fourier transform as input. In recent years, new algorithms based on deep learning have been proposed, providing breakthrough results that surpass the results of the classical methods. In this work we provide a novel deep learning architecture {PR}-{DAD} (Phase Retrieval Using Deep Auto- Decoders), whose components are carefully designed based on mathematical modeling of the phase retrieval problem. The architecture provides experimental results that surpass all current results.},
	author = {Gugel, Leon and Dekel, Shai},
	date = {2022-04-18},
	eprinttype = {arxiv},
	eprint = {2204.09051},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\P3HI57JJ\\2204.09051.pdf:application/pdf},
}

@article{zhuang_practical_2022,
	title = {Practical Phase Retrieval Using Double Deep Image Priors},
	url = {http://arxiv.org/abs/2211.00799},
	abstract = {Phase retrieval ({PR}) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in {PR} problems. We focus on the most difficult far-field {PR} ({FFPR}), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality.},
	author = {Zhuang, Zhong and Yang, David and Hofmann, Felix and Barmherzig, David and Sun, Ju},
	date = {2022-11-01},
	eprinttype = {arxiv},
	eprint = {2211.00799},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\QDS3UV8U\\2211.00799.pdf:application/pdf},
}

@report{fienup_phase_1982,
	title = {Phase retrieval algorithms: a comparison},
	abstract = {Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods. Both the problem of phase retrieval from two intensity measurements (in electron microscopy or wave front sensing) and the problem of phase retrieval from a single intensity measurement plus a non-negativity constraint (in astronomy) are considered, with emphasis on the latter. It is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm for the problem of two intensity measurements converge. The error-reduction algorithm is also shown to be closely related to the steepest-descent method. Other algorithms, including the input-output algorithm and the conjugate gradient method, are shown to converge in practice much faster than the error-reduction algorithm. Examples are shown.},
	author = {Fienup, J R},
	date = {1982},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\F32C857X\\ao-21-15-2758.pdf:application/pdf},
}

@article{shenoy_exact_2016,
	title = {Exact phase retrieval in principal shift-invariant spaces},
	volume = {64},
	issn = {1053587X},
	doi = {10.1109/TSP.2015.2481871},
	abstract = {We address the problem of phase retrieval from Fourier transform magnitude spectrum for continuous-Time signals that lie in a shift-invariant space spanned by integer shifts of a generator kernel. The phase retrieval problem for such signals is formulated as one of reconstructing the combining coefficients in the shift-invariant basis expansion. We develop sufficient conditions on the coefficients and the bases to guarantee exact phase retrieval, by which we mean reconstruction up to a global phase factor. We present a new class of discrete-domain signals that are not necessarily minimum-phase, but allow for exact phase retrieval from their Fourier magnitude spectra. We also establish Hilbert transform relations between log-magnitude and phase spectra for this class of discrete signals. It turns out that the corresponding continuous-domain counterparts need not satisfy a Hilbert transform relation; notwithstanding, the continuous-domain signals can be reconstructed from their Fourier magnitude spectra. We validate the reconstruction guarantees through simulations for some important classes of signals such as bandlimited signals and piecewise-smooth signals. We also present an application of the proposed phase retrieval technique for artifact-free signal reconstruction in frequency-domain optical-coherence tomography ({FDOCT}).},
	pages = {406--416},
	number = {2},
	journaltitle = {{IEEE} Transactions on Signal Processing},
	author = {Shenoy, Basty Ajay and Mulleti, Satish and Seelamantula, Chandra Sekhar},
	date = {2016-01-15},
	note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
	keywords = {Frequency-Domain Optical-Coherence Tomography, Hilbert Transform, Minimumphase Signals, Phase Retrieval, Shift-Invariant Space},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\KEGALL98\\Exact_Phase_Retrieval_in_Principal_Shift-Invariant_Spaces.pdf:application/pdf},
}

@report{fienup_reconstruction_1978,
	title = {Reconstruction of an object from the modulus of its Fourier transform},
	abstract = {We present a digital method for solving the phase-retrieval problem of optical-coherence theory: the reconstruction of a general object from the modulus of its Fourier transform. This technique should be useful for obtaining high-resolution imagery from interferometer data.},
	author = {Fienup, J R},
	date = {1978},
	note = {Publication Title: {OPTICS} {LETTERS}
Volume: 3
Issue: 1},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\GN3C2FDX\\Fienup - 1978 - Reconstruction of an object from the modulus of it.pdf:application/pdf},
}

@report{picinbono_instantaneous_1997,
	title = {On Instantaneous Amplitude and Phase of Signals},
	url = {https://hal.archives-ouvertes.fr/hal-01684083},
	abstract = {In many questions of signal processing, it is important to use the concepts of instantaneous amplitude or phase of signals. This is especially the case in communication systems with amplitude or frequency modulation. These concepts are often introduced empirically. However, it is well known that the correct approach for this purpose is to use the concept of analytic signal. Starting from this point, we show some examples of contradictions appearing when using other definitions of instantaneous amplitude or frequency that are commonly admitted. This introduces the problem of characterizing pure amplitude-modulated or pure phase-modulated signals. It is especially shown that whereas amplitude modulated signals can be characterized by spectral considerations, this is no longer the case for phase modulated signals.},
	pages = {552--560},
	author = {Picinbono, Bernard},
	date = {1997},
	note = {Publication Title: {IEEE} Trans. Sign. Proc
Volume: 45
Issue: 3},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\K86SHEI6\\INST. AMPL. PHASE.pdf:application/pdf},
}

@report{nikulin_model-independent_1996,
	title = {Model-independent determination of the strain distribution for a Si 0.9 Ge 0.1 /Si superlattice using x-ray diffractometry data},
	abstract = {The strain distribution in a Si 0.9 Ge 0.1 /Si superlattice is determined from x-ray diffractometry data with a 25 Å depth resolution. A logarithmic dispersion relation is used to determine the phase of the structure factor with information available a priori on the sample structure. Phase information is obtained from the observed reflection intensity via a logarithmic Hilbert transform and the a priori information is used to select the zeros to be included in the solution. The reconstructed lattice strain profile clearly resolves {SiGe} and Si layers of 90-160 Å thickness alternately stacked on a silicon substrate. The {SiGe} layer is found to have a lattice spacing in the surface-normal direction significantly smaller than predicted by Vegard's law. The result gives simulated rocking-curve profiles in very good agreement with the observation. The apparent deviation from Vegard's law could be confirmed by chemical analysis.},
	author = {Nikulin, A Yu and Stevenson, A W and Hashizume, H},
	date = {1996},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\XXFMJ7VB\\INTERESSANTE_PhysRevB.53.8277.pdf:application/pdf},
}

@report{nikulin_uniqueness_1998,
	title = {Uniqueness of the complex diffraction amplitude in x-ray Bragg diffraction},
	abstract = {The concept of the complex diffraction amplitude for x-ray Bragg diffraction is discussed in terms of a unique product of its zeros. This formalism allows the inverse scattering problem in x-ray Bragg diffraction to be solved unambiguously. The phase-retrieval technique, via a logarithmic dispersion relation, has associated with it the problem of localization of zeros of the complex diffraction amplitude. The mathematical approach predicts an infinite number of zeros of the complex diffraction amplitude. However, a physical discrete representation of the inversion technique limits the number of zeros that should be considered and allows one to obtain a unique solution for the structure-factor profile. Practical examples of the analytical continuation of the complex diffraction amplitude are presented. Distinctions between the artificial, mathematical, and the true, physical, features of the analytical continuation are elucidated. S0163-18299807017-9},
	author = {Nikulin, A Yu},
	date = {1998},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\CCGIH8CY\\INTERESSANTISSIMOPhysRevB.57.11178.pdf:application/pdf},
}

@report{nakajima_phase_1988,
	title = {Phase retrieval using the logarithmic Hilbert transform and the Fourier-series expansion},
	abstract = {Previously it was shown that one can solve the phase-retrieval problem from two intensities observed at the Fourier-transform plane of an object in one dimension by using the Fourier-series expansion. In this paper, an improved method using the logarithmic Hilbert transform and the Fourier series expansion is proposed. It is proved from the distribution of zeros in the complex plane that the Fourier-transform phase of Hermitian object functions cannot be retrieved by using the previous method but can be retrieved by using the method in this paper. The results reconstructed by the present method are also shown in computer simulations.},
	author = {Nakajima, N},
	date = {1988},
	note = {Publication Title: J. Opt. Soc. Am. A
Volume: 5
Issue: 2},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\SNLU8PTG\\josaa-5-2-257.pdf:application/pdf},
}

@report{guizar-sicairos_understanding_2012,
	title = {Understanding the twin-image problem in phase retrieval},
	abstract = {The twin-image problem in phase retrieval is characterized by the simultaneous occurrence of features from the original object and its inversion about the origin (twin image). This problem can occur in reconstructions for which the object support is centrosymmetric or loose, and in severe cases it can greatly hinder image quality. In this paper we examine this problem and find that it arises when the retrieved Fourier-domain phase is divided into sets of regions, some of which reconstruct the object while others the twin. We examine sample reconstructions that present the twin-image problem to different extents and find that, even when the twin-image problem is not visually evident, it can exist in small regions of the retrieved Fourier phase. The reduced-support constraint approach is shown to be effective in escaping stagnation caused by the twin-image problem.},
	author = {Guizar-Sicairos, Manuel and Fienup, James R},
	date = {2012},
	keywords = {0700070, 1003010, 1107440, {OCIS} codes: 1005070},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\2TCRX49V\\josaa-29-11-2367.pdf:application/pdf},
}

@report{mandel_hilbert_1995,
	title = {Hilbert and Blaschke phases in the temporal coherence function of stationary broadband light},
	abstract = {We show that the minimal phase of the temporal coherence function γ (τ) of stationary light having a partially-coherent symmetric spectral peak can be computed as a relative logarithmic Hilbert transform of its amplitude with respect to its asymptotic behavior. The procedure is applied to experimental data from amplified spontaneous emission broadband sources in the 1.55 μm band with subpicosecond coherence times, providing examples of degrees of coherence with both minimal and non-minimal phase. In the latter case, the Blaschke phase is retrieved and the position of the Blaschke zeros determined.},
	pages = {46--49},
	institution = {Kluwer Ac. Pub},
	author = {Mandel, L and Wolf, E},
	date = {1995},
	keywords = {(0306600) Statistical optics, (0605625) Radio frequency photonics, (1005070) Phase retrieval, {OCIS} codes: (0301640) Coherence},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\P83UBFLN\\oe-16-22-18397.pdf:application/pdf},
}

@article{li_phase_2016,
	title = {Phase retrieval by using the transport-of-intensity equation with Hilbert transform},
	volume = {41},
	issn = {0146-9592},
	doi = {10.1364/ol.41.001616},
	abstract = {Phase recovery by solving the transport-of-intensity equation ({TIE}) is a non-iterative and non-interferometric phase retrieval technique. From solving the {TIE} with conventional, one partial derivative and Hilbert transform methods for both the periodic and aperiodic samples, we demonstrate that the Hilbert transform method can provide the smoother phase images with edge enhancement and fine structures. Furthermore, compared with the images measured by optical and atomic force microscopy, the Hilbert transform method has the ability to quantitatively map out the phase images for both the periodic and aperiodic structures.},
	pages = {1616},
	number = {7},
	journaltitle = {Optics Letters},
	author = {Li, Wei-Shuo and Chen, Chun-Wei and Lin, Kuo-Feng and Chen, Hou-Ren and Tsai, Chih-Ya and Chen, Chyong-Hua and Hsieh, Wen-Feng},
	date = {2016-04-01},
	note = {Publisher: The Optical Society},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\SSDSAAAD\\ol-41-7-1616.pdf:application/pdf},
}

@article{hirose_use_2017,
	title = {Use of Kramers–Kronig relation in phase retrieval calculation in X-ray spectro-ptychography},
	volume = {25},
	issn = {10944087},
	doi = {10.1364/oe.25.008593},
	abstract = {© 2017 Optical Society of America. Coherent diffraction imaging ({CDI}) is a method for reconstructing the complex-valued image of an object from diffraction intensities by using iterative phasing methods. X-ray ptychography is a scanning type of {CDI} using X-rays, allowing us to visualize the complex transmission function of an extended specimen. We here propose the use of the Kramers-Kronig relation ({KKR}) as an additional constraint in phase retrieval algorithms for multiple-energy X-ray ptychography using the absorption edge of a specific element. A numerical simulation showed that the speed of convergence was increased by using the improved algorithm with the {KKR}. We successfully demonstrated its usefulness in a proof-of-principle experiment at {SPring}-8. The present algorithm is particularly useful for imaging X-ray absorption fine structures of a specific element buried within thick samples by hard X-ray spectro-ptychography.},
	pages = {8593},
	number = {8},
	journaltitle = {Optics Express},
	author = {Hirose, Makoto and Shimomura, Kei and Burdet, Nicolas and Takahashi, Yukio},
	date = {2017-04-17},
	pmid = {28437937},
	note = {Publisher: The Optical Society},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\LR6ZZVDE\\oe-25-8-8593.pdf:application/pdf},
}

@article{bond_sampling_1958,
	title = {On Sampling the Zeros of Bandwidth Limited Signals},
	volume = {4},
	issn = {21682712},
	doi = {10.1109/TIT.1958.1057457},
	abstract = {The sampling theorem enables a band-limited signal to be expressed in terms of a set of sample point values, which occur at the Nyquist rate. The sampling theorem has been generalized to include nonuniform sampling and the use of derivatives of the signal. In the present paper, a sampling theorem has been developed which utilizes information related to the zeros of the signal. The concept of complex zeros is introduced to show that the zeros occur at the Nyquist rate. This sampling theorem can be of use for enabling the transmission of binary signals, such as facsimile and infinitely-clipped speech, over a continuous band-limited channel. The result indicates the desirability of developing a completely general theory of sampling applicable to the various situations which may arise in practice. © 1958, {IEEE}. All rights reserved.},
	pages = {110--113},
	number = {3},
	journaltitle = {{IRE} Transactions on Information Theory},
	author = {Bond, F. E. and Cahn, C. R.},
	date = {1958},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\2FBHTFK7\\On_the_sampling_the_zeros_of_bandwidth_limited_signals.pdf:application/pdf},
}

@report{xxxvi_english_1957,
	title = {English translation: Soy. Phys. {JEFF}},
	abstract = {From correlation measurements one can determine the modulus ly(r) l (for real v) of the cpherence functio{\textasciitilde} which is the Fourier transform of the spectral density. It is shown that by using, exponential filters one can determine l y({\textasciitilde})l for complex {\textasciitilde} also. From these measurements and u{\textasciitilde}ing analytic properties of y(v), one can determine y(w) (i.e. its phase also) and hence the spectral density. Recently there h{\textasciitilde}ve been a number of publications ({\textasciitilde}-7) in various branches of physics concerning the problem of determining the phase of a certain function (analytic in half of the complex plane bounded by the real axis) from the knowledge of its modulus on the real axis. This problem arises in a number of physical situations. For example in interference or correlation spectroscopy where one wishes to obtain information about the distribution of energy in-the sp3ctrum of {\textasciitilde} light beam from measurements of the visibility of inter-(*) This rdsearch was supported by the Army Research Office, Durham. (1) L.},
	pages = {1452},
	author = {Xxxvi, Vol},
	date = {1957},
	note = {Publication Title: {KANO} and E. {WOLF}: Prec. {\textasciitilde}Phys. See
Volume: 33
Issue: 8},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\BS7KWYG9\\Phase retrieval with exponential filters.pdf:application/pdf},
}

@inproceedings{taylor_phase_2003,
	title = {The phase problem},
	volume = {59},
	doi = {10.1107/S0907444903017815},
	abstract = {Given recent advances in phasing methods, those new to protein crystallography may be forgiven for asking 'what problem?'. As many of those attending the {CCP}4 meeting come from a biological background, struggling with expression and crystallization, this introductory paper aims to introduce some of the basics that will hopefully make the subsequent papers penetrable. What is the 'phase' in crystallography? What is 'the problem'? How can we overcome the problem? The paper will emphasize that the phase values can only be discovered through some prior knowledge of the structure. The paper will canter through direct methods, isomorphous replacement, anomalous scattering and molecular replacement. As phasing is the most acronymic realm of crystallography, {MR}, {SIR}, {SIRAS}, {MIR}, {MIRAS}, {MAD} and {SAD} will be expanded and explained in part. Along the way, we will meet some of the heroes of protein crystallography such as Perutz, Kendrew, Crick, Rossmann and Blow who established many of the phasing methods in the {UK}. It is inevitable that some basic mathematics is encountered, but this will be done as gently as possible.},
	pages = {1881--1890},
	booktitle = {Acta Crystallographica - Section D Biological Crystallography},
	author = {Taylor, Garry},
	date = {2003-11},
	pmid = {14573942},
	note = {Issue: 11
{ISSN}: 09074449},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\PKRXN5NF\\phase_problem_suits.pdf:application/pdf},
}

@article{maddali_phase_nodate,
	title = {Phase retrieval for Bragg coherent diffraction imaging at high x-ray energies},
	url = {https://hal-amu.archives-ouvertes.fr/hal-02139472},
	doi = {10.1103/PhysRevA.99.053838ï},
	abstract = {Coherent x-ray beams with energies 50 {keV} can potentially enable three-dimensional imaging of atomic lattice distortion fields within individual crystallites in bulk polycrystalline materials through Bragg coherent diffraction imaging ({BCDI}). However, the undersampling of the diffraction signal due to Fourier-space compression at high x-ray energies renders conventional phase-retrieval algorithms unsuitable for three-dimensional reconstruction. To address this problem, we utilize a phase-retrieval method with a Fourier constraint specifically tailored for undersampled diffraction data measured with coarse-pitched detector pixels that bin the underlying signal. With our approach, we show that it is possible to reconstruct three-dimensional strained crystallites from an undersampled Bragg diffraction data set subject to pixel-area integration without having to physically upsample the diffraction signal. Using simulations and experimental results, we demonstrate that explicit modeling of Fourier-space compression during phase retrieval provides a viable means by which to invert high-energy {BCDI} data, which is otherwise intractable.},
	author = {Maddali, S and Allain, Marc and Cha, W and Harder, R and Park, S and Kenesei, P and Almer, J and Nashed, Y and Hruszkewycz, S and Allain, M and Park, J.-S and Hruszkewycz, S O},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\S6VV3ZKR\\PhysRevA.99.053838.pdf:application/pdf},
}

@article{burge_phase_1976,
	title = {{PHASE} {PROBLEM}.},
	volume = {350},
	doi = {10.1098/rspa.1976.0103},
	abstract = {The paper discusses the use of the theory of entire functions for solving the phase problem. The uniqueness of the phase obtained from a logarithmic Hilbert transform is investigated and the difficulties due to the presence of zeros in the complex plane are discussed. Methods are put forward for both the removal of the zeros and, when this is not possible, for locating them in order to include their effect. The paper analyzes known experimental methods for phase determination.},
	pages = {191--212},
	number = {1661},
	journaltitle = {Proc R Soc London Ser A},
	author = {Burge, R. E. and Fiddy, M. A. and Greenaway, A. H. and Ross, G.},
	date = {1976},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\MUHLUTCI\\rspa.1976.0103.pdf:application/pdf},
}

@report{burge_application_1974,
	title = {The application of dispersion relations (Hilbert transforms) to phase retrieval},
	abstract = {A method is given for the retrieval of phase from amplitude information using the Hilbert transform, without the need for evaluating Blaschke factors. The connection between causality and dispersion relations is well known (Toll 1956). Dispersion relations connect, for example, the real part of a function to an integral involving the imaginary part. Such relations occur in the form of the Kramers-Kronig relation in optics (Loudon 1973), in particle scattering (van Kampen 1953, Hilgevoord 1960, Roman 1965), in electron optics (Misell et al 1974), and in communication theory (Kuo and Freeny 1962). This integral relation may be defined by the Hilbert transform for a function y (t) : where P denotes the Cauchy principal value, It has been shown (Titchmarsh 1948) that equations (1) and (2) follow if the Fourier transform of y (t) vanishes for negative values of its argument, and y (t) is the limit as T +. t of an analytic function y ({\textasciitilde}) , where T = t + is, which is regular for s {\textgreater} 0. Equations (1) and (2) have been used to determine the phase \$(t) of y (t) when an estimate of Re [ y (t) ] (or Im [ y (t) ]) is available (Misell et a1 1974). In general only Iy(t)/ is measured experimentally, and it is of value to explore the direct determination of \$(t) from Iy(t)l (Page 1955, Toll 1956, Pefina 1971). The minimal phase \$(t) may be determined from the equation (see, for example, Pefina 1971). In general there are two sources of additional phase factors to \&t): (i) the product of Blaschke phase factors arising from the zeros of y ({\textasciitilde}) in the upper half-plane (s{\textgreater}O); (ii) an additive phase factor, linearly dependent on t (van Kampen 1953, Toll 1956), which reflects the fact that only Iy(t)l is known on the real axis. This second factor follows since Iy(t)l {IG}(t)l is indistinguishable from Iy(t)l, if G(T) is any bounded analytic function in the upper half-plane and {IG}(T)I = 1, almost 61 L65},
	author = {Burge, R E and Fiddy, M A and Greenaway, A H and Ross, G},
	date = {1974},
	note = {Publication Title: J. Phys. D: {AppI}. Phys
Volume: 7},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9L5SKHCC\\R_E_Burge_1974_J._Phys._D _Appl._Phys._7_L65.pdf:application/pdf},
}

@article{zenkova_phase_2015,
	title = {Phase retrieval of speckle fields based on 2D Hilbert transform},
	volume = {24},
	issn = {19347898},
	doi = {10.3103/S1060992X15040074},
	abstract = {The use of a “window” 2D Hilbert transform for reconstruction of the phase distribution of remote objects is proposed. It is shown that the advantage of this approach consists in the invariance of a phase map to a change of the position of the kernel of transformation and in a possibility to reconstruct the structure-forming elements of the skeleton of an optical field, including singular points and saddle points. We demonstrate the possibility to reconstruct the equi-phase lines within a narrow confidence interval, and introduce a new algorithm for solving the phase problem for random 2D intensity distributions.},
	pages = {303--308},
	number = {4},
	journaltitle = {Optical Memory and Neural Networks (Information Optics)},
	author = {Zenkova, C. Yu and Gorsky, M. P. and Ryabyj, P. A.},
	date = {2015-10-01},
	note = {Publisher: Allerton Press Incorporation},
	keywords = {Hilbert-transform, phase retrieval, speckle pattern},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9ZLWEXYK\\S1060992X15040074.pdf:application/pdf},
}

@article{taylor_phase_1981,
	title = {The Phase Retrieval Problem},
	volume = {29},
	issn = {15582221},
	doi = {10.1109/TAP.1981.1142559},
	abstract = {The phase retrieval problem arises in applications of electromagnetic theory in which wave phase is apparently lost or impractical to measure and only intensity data are available. The mathematics of the problem provides unusual insights into the nature of electromagnetic fields. The theory is reviewed and illustrated. The basic issue of the phase retrieval problem, stated for a one-di-mensional field, is that although a unique Fourier transform relation exists between the field F(x) in the Fraunhofer plane and the field u(x') in the object plane, the infinite fold phase ambiguity which appears as the result of the possibilities of conjugating the zeros of F(z), z = x + jy implies that additional information or processing of the object wave must be available to obtain the phase. Among the possible solutions which are described are reference beam addition, apodization and the use of multiple intensity distributions, permitting the use of iterative computational procedures in some applications. Copyright © 1981 by The Institute of Electrical and Electronics Engineers, Inc.},
	pages = {386--391},
	number = {2},
	journaltitle = {{IEEE} Transactions on Antennas and Propagation},
	author = {Taylor, Leonard S.},
	date = {1981},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\IASQA956\\The_phase_retrieval_problem.pdf:application/pdf},
}

@article{chen_unsupervised_2022,
	title = {Unsupervised Phase Retrieval Using Deep Approximate {MMSE} Estimation},
	volume = {70},
	issn = {19410476},
	doi = {10.1109/TSP.2022.3170710},
	abstract = {Phase retrieval ({PR}) is about reconstructing a signal from the magnitude of a number of its complex-valued linear measurements. Recent rapid progress has been made on the development of neural network ({NN}) based methods for {PR}. Most of these methods employ pre-trained {NNs} for modeling target signals, and they require collecting large-scale datasets with ground-truth signals for pre-training, which can be very challenging in many scenarios. There are a few unsupervised learning methods employing untrained {NN} priors for {PR} which avoid using external datasets; however, their performance is unsatisfactory compared to pre-trained-{NN}-based methods. This paper proposes an unsupervised learning method for {PR} which does not rely on pre-trained {NNs} while providing state-of-the-art performance. The proposed method trains a randomly-initialized generative {NN} for signal reconstruction directly on the magnitude measurements of a target signal, which approximates the minimum mean squared error estimator via dropout-based model averaging. Such a model-averaging-based approach provides a better internal prior for the target signal than existing untrained-{NN}-based methods. The experiments on image reconstruction demonstrate both the advantage of our method over existing unsupervised methods and its competitive performance to pre-trained-{NN}-based methods.},
	pages = {2239--2252},
	journaltitle = {{IEEE} Transactions on Signal Processing},
	author = {Chen, Mingqin and Lin, Peikang and Quan, Yuhui and Pang, Tongyao and Ji, Hui},
	date = {2022},
	note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
	keywords = {Inverse problems, Phase retrieval, Unsupervised learning, Untrained neural networks},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\V2I9KSUY\\Unsupervised_Phase_Retrieval_Using_Deep_Approximate_MMSE_Estimation.pdf:application/pdf},
}

@report{goos_lncs_nodate,
	title = {{LNCS} 4633 - Image Analysis and Recognition},
	author = {Goos, Gerhard and Hartmanis, Juris and Van, Jan and Board, Leeuwen Editorial and Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M and Mattern, Friedemann and Zurich, Eth and Mitchell, John C and Naor, Moni and Nierstrasz, Oscar and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y and Weikum, Gerhard},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\JPDBSLI7\\2D - Hilbert.pdf:application/pdf},
}

@article{mokhtar_simulation_2022,
	title = {Simulation of Bragg coherent diffraction imaging},
	volume = {6},
	issn = {23996528},
	doi = {10.1088/2399-6528/ac6ab0},
	abstract = {The arrangement of atoms within a crystal and information on deviations from the ideal lattice is encoded in the diffraction pattern obtained from an appropriately conducted Bragg coherent diffraction imaging ({BCDI}) experiment. A foreknowledge of how specific displacements of atoms within the unit cell alter the {BCDI} diffraction pattern and the subsequent real-space image is often useful for interpretation and can provide valuable insight for materials design. Here we report on an atomistic approach to efficiently simulate {BCDI} diffraction patterns by factorising and eliminating certain redundancies in the conventional approach. Our method is able to reduce the computation time by several orders of magnitude without compromising the recovered phase information and therefore enables feasible atomistic simulations on nanoscale crystals with arbitrary lattice distortions.},
	number = {5},
	journaltitle = {Journal of Physics Communications},
	author = {Mokhtar, A. H. and Serban, D. and Newton, M. C.},
	date = {2022-05-01},
	note = {Publisher: Institute of Physics},
	keywords = {synchrotron radiation, x-ray imaging, x-ray microscopy},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\ZWJ5H9JS\\Mokhtar_2022_J._Phys._Commun._6_055003.pdf:application/pdf},
}

@article{judge_defect_2022,
	title = {Defect identification in simulated Bragg coherent diffraction imaging by automated {AI}},
	issn = {08837694},
	doi = {10.1557/s43577-022-00342-1},
	abstract = {Abstract: X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. We compare different approaches to designing neural networks for this application and demonstrate the potential of automated {ML} ({autoML}) approaches. Impact statement: The need for automated processing of coherent diffraction data is strongly motivated by the advent of fourth-generation synchrotron x-ray sources, where coherent diffraction data will be generated at a tremendous rate and human interaction with data analysis, and especially iterative phase retrieval image reconstruction, will become untenable. Our approach provides a path to dealing with this necessary improvement in data processing efficiency. We expect that this work, which demonstrates the applicability of automated machine learning to x-ray analysis, will be of broad interest to scientists and users of synchrotron and {XFEL} facilities. Graphical abstract: [Figure not available: see fulltext.]},
	journaltitle = {{MRS} Bulletin},
	author = {Judge, William and Chan, Henry and Sankaranarayanan, Subramanian and Harder, Ross J. and Cabana, Jordi and Cherukara, Mathew J.},
	date = {2022},
	note = {Publisher: Springer Nature},
	keywords = {Bulk techniques, Composition and microstructure, Computing, Machine learning, Material type, Nanoscale, Performance, Simulation, x-ray diffraction ({XRD})},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\83YCATXU\\Defect identifcation in simulated .pdf:application/pdf},
}

@article{jossou_three-dimensional_nodate,
	title = {Three-dimensional strain imaging of irradiated chromium using multi-reeection Bragg coherent diffraction},
	url = {https://doi.org/10.21203/rs.3.rs-1816243/v1},
	doi = {10.21203/rs.3.rs-1816243/v1},
	author = {Jossou, Ericmoore and Assefa, Tadesse and Suzana, Ana and Campbell, Colleen and Harder, Ross and Kisslinger, Kim and Sun, Cheng and Gan, Jian and Ecker, Lynne and Robinson, Ian},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\MAB2PQUR\\Three-dimensional strain imaging of irradiated.pdf:application/pdf},
}

@article{maddali_differentiable_2022,
	title = {A differentiable forward model for the concurrent, multi-peak Bragg coherent x-ray diffraction imaging problem},
	url = {http://arxiv.org/abs/2208.00970},
	abstract = {We present a general analytic approach to spatially resolve the nano-scale lattice distortion field of strained and defected compact crystals with Bragg coherent x-ray diffraction imaging ({BCDI}). Our approach relies on fitting a differentiable forward model simultaneously to multiple {BCDI} datasets corresponding to independent Bragg reflections from the same single crystal. It is designed to be faithful to heterogeneities that potentially manifest as phase discontinuities in the coherently diffracted wave, such as lattice dislocations in an imperfect crystal. We retain fidelity to such small features in the reconstruction process through a Fourier transform -based resampling algorithm designed to largely avoid the point spread tendencies of commonly employed interpolation methods. The reconstruction model defined in this manner brings {BCDI} reconstruction into the scope of explicit optimization driven by automatic differentiation. With results from simulations and experimental diffraction data, we demonstrate significant improvement in the final image quality compared to conventional phase retrieval, enabled by explicitly coupling multiple {BCDI} datasets into the reconstruction loss function.},
	author = {Maddali, S. and Frazer, T. D. and Delegan, N. and Harmon, K. J. and Sullivan, S. E. and Allain, M. and Cha, W. and Dibos, A. and Poudyal, I. and Kandel, S. and Nashed, Y. S. G. and Heremans, F. J. and You, H. and Cao, Y. and Hruszkewycz, S. O.},
	date = {2022-08-01},
	eprinttype = {arxiv},
	eprint = {2208.00970},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\HTDA58ME\\A differentiable forward model for the concurrent, multi-peak Bragg.pdf:application/pdf},
}

@article{cherukara_anisotropic_2018,
	title = {Anisotropic nano-scale resolution in 3D Bragg coherent diffraction imaging},
	volume = {113},
	issn = {00036951},
	doi = {10.1063/1.5055235},
	abstract = {We demonstrate that the resolution of three-dimensional (3D) real-space images obtained from Bragg coherent x-ray diffraction measurements is direction dependent. We propose and demonstrate the effectiveness of a metric to determine the spatial resolution of images that accounts for the directional dependence. The measured direction dependent resolution of ∼4-9 nm is higher than the best previously obtained 3D measurements. Finally, we quantify the relationship between the resolution of recovered real-space images and dosage and discuss its implications in the light of next generation synchrotrons.},
	number = {20},
	journaltitle = {Applied Physics Letters},
	author = {Cherukara, Mathew J. and Cha, Wonsuk and Harder, Ross J.},
	date = {2018-11-12},
	eprinttype = {arxiv},
	eprint = {1806.02898},
	note = {Publisher: American Institute of Physics Inc.},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\5PA2EVF7\\Anisotropic nano-scale resolution in 3D 2018.pdf:application/pdf},
}

@article{favre-nicolin_analysis_2010,
	title = {Analysis of strain and stacking faults in single nanowires using Bragg coherent diffraction imaging},
	volume = {12},
	issn = {13672630},
	doi = {10.1088/1367-2630/12/3/035013},
	abstract = {Coherent diffraction imaging ({CDI}) on Bragg reflections is a promising technique for the study of three-dimensional (3D) composition and strain fields in nanostructures, which can be recovered directly from the coherent diffraction data recorded on single objects. In this paper, we report results obtained for single homogeneous and heterogeneous nanowires with a diameter smaller than 100 nm, for which we used {CDI} to retrieve information about deformation and faults existing in these wires. We also discuss the influence of stacking faults, which can create artefacts during the reconstruction of the nanowire shape and deformation. © {IOP} Publishing Ltd and Deutsche Physikalische Gesellschaft.},
	journaltitle = {New Journal of Physics},
	author = {Favre-Nicolin, V. and Mastropietro, F. and Eymery, J. and Camacho, D. and Niquet, Y. M. and Borg, B. M. and Messing, M. E. and Wernersson, L. E. and Algra, R. E. and Bakkers, E. P.A.M. and Metzger, T. H. and Harder, R. and Robinson, I. K.},
	date = {2010-03-31},
	note = {Publisher: Institute of Physics Publishing},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\NUMIUU9Z\\Favre-Nicolin et al. - 2010 - Analysis of strain and stacking faults in single n.pdf:application/pdf},
}

@article{hofmann_3d_2017,
	title = {3D lattice distortions and defect structures in ion-implanted nano-crystals},
	volume = {7},
	issn = {20452322},
	doi = {10.1038/srep45993},
	abstract = {Focussed Ion Beam ({FIB}) milling is a mainstay of nano-scale machining. By manipulating a tightly focussed beam of energetic ions, often gallium (Ga +), {FIB} can sculpt nanostructures via localised sputtering. This ability to cut solid matter on the nano-scale revolutionised sample preparation across the life, earth and materials sciences. Despite its widespread usage, detailed understanding of the {FIB}-induced structural damage, intrinsic to the technique, remains elusive. Here we examine the defects caused by {FIB} in initially pristine objects. Using Bragg Coherent X-ray Diffraction Imaging ({BCDI}), we are able to spatially-resolve the full lattice strain tensor in {FIB}-milled gold nano-crystals. We find that every use of {FIB} causes large lattice distortions. Even very low ion doses, typical of {FIB} imaging and previously thought negligible, have a dramatic effect. Our results are consistent with a damage microstructure dominated by vacancies, highlighting the importance of free-surfaces in determining which defects are retained. At larger ion fluences, used during {FIB}-milling, we observe an extended dislocation network that causes stresses far beyond the bulk tensile strength of gold. These observations provide new fundamental insight into the nature of the damage created and the defects that lead to a surprisingly inhomogeneous morphology.},
	journaltitle = {Scientific Reports},
	author = {Hofmann, Felix and Tarleton, Edmund and Harder, Ross J. and Phillips, Nicholas W. and Ma, Pui Wai and Clark, Jesse N. and Robinson, Ian K. and Abbey, Brian and Liu, Wenjun and Beck, Christian E.},
	date = {2017-04-06},
	pmid = {28383028},
	note = {Publisher: Nature Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\T4KA75LQ\\Hofmann et al. - 2017 - 3D lattice distortions and defect structures in io.pdf:application/pdf},
}

@report{grubel_correlation_2001,
	title = {Correlation spectroscopy with coherent X-rays},
	abstract = {The longitudinal coherence function at the Advanced Photon Source beamline 34-{ID}-C has been measured by a novel method and the coherence length (ξ L) determined to be, ξ L = 0.66 ± 0.02µm. Three dimensional Coherent X-ray Diffraction ({CXD}) patterns were measured for multiple Bragg reflections from two Zinc Oxide ({ZnO}) nanorods with differing aspect ratios. The visibility of fringes corresponding to the 002 crystal direction for each reflection were found to be different and used to map the coherence function of the incident radiation. Partial coherence was found to be associated with amplitude 'hot' spots in three dimensional reconstructions of the crystal structure.},
	pages = {1--2},
	author = {Grübel, G and Zontone, F},
	date = {2001},
	note = {Publication Title: J. Phys. Condens. Matter
Volume: 13
Issue: 47},
	keywords = {(1005070) Phase retrieval, (1107440) X-ray imaging, {OCIS} codes: (0301640) Coherence},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\KSGSSB3V\\Leake et al. - 2009 - Longitudinal coherence function in X-ray imaging o.pdf:application/pdf},
}

@report{nikulin_reply_nodate,
	title = {Reply to ''Comment on 'Uniqueness of the complex diffraction amplitude in x-ray Bragg diffraction' ''},
	author = {Nikulin, A Yu},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\X8U2EAXW\\PhysRevB.59.14784.pdf:application/pdf},
}

@article{ameh_review_2019,
	title = {A review of basic crystallography and x-ray diffraction applications},
	volume = {105},
	issn = {14333015},
	doi = {10.1007/s00170-019-04508-1},
	abstract = {Although various researched works have been carried out in x-ray crystallography and its applications, but there are still limited number of researches on crystallographic theories and industrial application of x-ray diffraction. The present study reviewed and provided detailed discussion on atomic arrangement of single crystals, mathematical concept of Bravais, reciprocal lattice, and application of x-ray diffraction. Determination of phase identification, crystal structure, dislocation density, crystallographic orientation, and gran size using x-ray diffraction peak intensity, peak position, and peak width were discussed. The detailed review of crystallographic theories and x-ray diffraction application would benefit majorly engineers and specialists in chemical, mining, iron, and steel industries.},
	pages = {3289--3302},
	number = {7},
	journaltitle = {International Journal of Advanced Manufacturing Technology},
	author = {Ameh, E. S.},
	date = {2019-12-01},
	note = {Publisher: Springer},
	keywords = {Crystal structure, Crystallography, Intensity, Lattice, Unit cell, X-ray diffraction},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9TCPXV3S\\physics_diffraction.pdf:application/pdf},
}

@article{dupraz_signature_2015,
	title = {Signature of dislocations and stacking faults of face-centred cubic nanocrystals in coherent X-ray diffraction patterns: A numerical study},
	volume = {48},
	issn = {16005767},
	doi = {10.1107/S1600576715005324},
	abstract = {Crystal defects induce strong distortions in diffraction patterns. A single defect alone can yield strong and fine features that are observed in high-resolution diffraction experiments such as coherent X-ray diffraction. The case of face-centred cubic nanocrystals is studied numerically and the signatures of typical defects close to Bragg positions are identified. Crystals of a few tens of nanometres are modelled with realistic atomic potentials and 'relaxed' after introduction of well defined defects such as pure screw or edge dislocations, or Frank or prismatic loops. Diffraction patterns calculated in the kinematic approximation reveal various signatures of the defects depending on the Miller indices. They are strongly modified by the dissociation of the dislocations. Selection rules on the Miller indices are provided, to observe the maximum effect of given crystal defects in the initial and relaxed configurations. The effect of several physical and geometrical parameters such as stacking fault energy, crystal shape and defect position are discussed. The method is illustrated on a complex structure resulting from the simulated nanoindentation of a gold nanocrystal.},
	pages = {621--644},
	journaltitle = {Journal of Applied Crystallography},
	author = {Dupraz, Maxime and Beutier, Guillaume and Rodney, David and Mordehai, Dan and Verdier, Marc},
	date = {2015-06-01},
	pmid = {26089755},
	note = {Publisher: International Union of Crystallography},
	keywords = {coherent X-ray diffraction, dislocations, face-centred cubic nanocrystals, stacking faults},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\2WXQTPPI\\nb5138.pdf:application/pdf},
}

@article{robinson_coherent_2009,
	title = {Coherent X-ray diffraction imaging of strain at the nanoscale},
	volume = {8},
	issn = {14764660},
	doi = {10.1038/nmat2400},
	abstract = {The understanding and management of strain is of fundamental importance in the design and implementation of materials. The strain properties of nanocrystalline materials are different from those of the bulk because of the strong influence of their surfaces and interfaces, which can be used to augment their function and introduce desirable characteristics. Here we explain how new X-ray diffraction techniques, which take advantage of the latest synchrotron radiation sources, can be used to obtain quantitative three-dimensional images of strain. These methods will lead, in the near future, to new knowledge of how nanomaterials behave within active devices and on unprecedented timescales. © 2009 Macmillan Publishers Limited. All rights reserved.},
	pages = {291--298},
	number = {4},
	journaltitle = {Nature Materials},
	author = {Robinson, Ian and Harder, Ross},
	date = {2009},
	note = {Publisher: Nature Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\ALYAE5KV\\Robinson et Harder - 2009 - Coherent X-ray diffraction imaging of strain at th.pdf:application/pdf},
}

@report{paganin_coherent_2006,
	title = {Coherent X-Ray Optics},
	author = {Paganin, David M},
	date = {2006},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\3XSBNMN6\\Paganin - 2006 - Coherent X-Ray Optics.pdf:application/pdf},
}

@report{sayre_extendibility_1998,
	title = {On the Extendibility of X-ray Crystallography to Noncrystals},
	abstract = {This paper discusses the concept that crystallinity is not an essential requirement for applying the techniques of • X-ray crystal structure analysis. Assuming this to be true, the removal of crystallinity as a prerequisite for the techniques would allow the imaging of structures well beyond the present range of sizes accessible to X-ray crystallography. An example of an imageable structure could be a single small biological cell, containing perhaps 1013-1014 Da. The proposed concept differs from the usual diffraction method of studying noncrystalline structure, i.e. small-angle scattering, in carrying out the diffraction experiment and subsequent processing as if the structure being studied were in fact the asymmetric unit of a crystal: i.e.: orienting the structure in all directions in the X-ray beam needed to explore its Fourier transform (F transform), and phasing and inverting the transform to obtain• the electron-density image of the structure. The one actual difference from the crystal case is that the F transform is faint and also continuous, rather than displaying discrete intense Bragg spots. As a result, to get a readable pattern, the structure must be exposed to high levels of radiation. This last fact creates the principal limitation of the technique. With single air-dried biological cells at room temperature as diffracting specimens and soft X-rays in the wavelength range 18-32 A, patterns to date have not been observed beyond resolutions of 140-300 A before radiation damage has become evident. At this resolution, the technique nevertheless would lie on the same curve of resolution vs {\textasciitilde}specimen size as do the existing majot" imaging techniques of X-ray crystallography, electron microscopy and light microscopy, falling directly between the latter two. Thus, X-ray diffractive imaging is not destroyed by the withdrawal of crystallinity but instead is shifted to a new size range of stmc3ures, which have hitherto been somewhat inaccessible to imaging. A method for phasing the diffraction pattern, based on the ability to sample the pattern more finely than in the case of the crystalline specimen, is giving good results in preliminary testing.},
	pages = {232--239},
	author = {Sayre, F - D and Chapman\{{\textbackslash}textasciitilde\} 'and, H N and Miao, J},
	date = {1998},
	note = {Publication Title: Acta Cryst
Volume: 54},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\5IC73RWB\\Sayre Chapman Miao 1998.pdf:application/pdf},
}

@article{walmsley_quantum_2015,
	title = {Quantum optics: Science and technology in a new light},
	volume = {348},
	issn = {10959203},
	doi = {10.1126/science.aab0097},
	abstract = {Light facilitates exploration of quantum phenomena that illuminate the basic properties of nature and also enables radical new technologies based on these phenomena. The critical features of quantum light that underpin the opportunities for discovery and application are exceptionally low noise and strong correlations. Rapid progress in both science and technology has been stimulated by adopting components developed for optical telecommunications and networking, such as highly efficient detectors, integrated photonic circuits, and waveguide- or nanostructure-based nonlinear optical devices. These provide the means to generate new quantum states of light and matter of unprecedented scale, containing many photons with quantum correlations across space and time. Notably, networks with only several tens of photons are already beyond what can be efficiently analyzed by current computers.},
	pages = {525--530},
	number = {6234},
	journaltitle = {Science},
	author = {Walmsley, I. A.},
	date = {2015-05-01},
	note = {Publisher: American Association for the Advancement of Science},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\TC8XZFPD\\science.aaa1394.pdf:application/pdf},
}

@article{dong_phase_2022,
	title = {Phase Retrieval: From Computational Imaging to Machine Learning},
	url = {http://arxiv.org/abs/2204.03554},
	abstract = {Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniques as varied as generic gradient-descent routines or specialized spectral methods, to name a few. Yet, the phase-recovery problem remains a challenge to this day. Recently, however, advances in machine learning have revitalized the study of phase retrieval in two ways: significant theoretical advances have emerged from the analogy between phase retrieval and single-layer neural networks; practical breakthroughs have been obtained thanks to deep-learning regularization. In this tutorial, we review phase retrieval under a unifying framework that encompasses classical and machine-learning methods. We focus on three key elements: applications, overview of recent reconstruction algorithms, and the latest theoretical results.},
	author = {Dong, Jonathan and Valzania, Lorenzo and Maillard, Antoine and Pham, Thanh-an and Gigan, Sylvain and Unser, Michael},
	date = {2022-04-07},
	eprinttype = {arxiv},
	eprint = {2204.03554},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\TC2Z57JS\\TOREAD.pdf:application/pdf},
}

@article{cherukara_real-time_2018,
	title = {Real-time coherent diffraction inversion using deep generative networks},
	volume = {8},
	issn = {20452322},
	doi = {10.1038/s41598-018-34525-1},
	abstract = {Phase retrieval, or the process of recovering phase information in reciprocal space to reconstruct images from measured intensity alone, is the underlying basis to a variety of imaging applications including coherent diffraction imaging ({CDI}). Typical phase retrieval algorithms are iterative in nature, and hence, are time-consuming and computationally expensive, making real-time imaging a challenge. Furthermore, iterative phase retrieval algorithms struggle to converge to the correct solution especially in the presence of strong phase structures. In this work, we demonstrate the training and testing of {CDI} {NN}, a pair of deep deconvolutional networks trained to predict structure and phase in real space of a 2D object from its corresponding far-field diffraction intensities alone. Once trained, {CDI} {NN} can invert a diffraction pattern to an image within a few milliseconds of compute time on a standard desktop machine, opening the door to real-time imaging.},
	number = {1},
	journaltitle = {Scientific Reports},
	author = {Cherukara, Mathew J. and Nashed, Youssef S.G. and Harder, Ross J.},
	date = {2018-12-01},
	pmid = {30410034},
	note = {Publisher: Nature Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\UQLHPPCS\\Cherukara et al. - 2018 - Real-time coherent diffraction inversion using dee.pdf:application/pdf},
}

@article{lim_convolutional_2021,
	title = {A convolutional neural network for defect classification in Bragg coherent X-ray diffraction},
	volume = {7},
	issn = {20573960},
	doi = {10.1038/s41524-021-00583-9},
	abstract = {Coherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials. These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type (screw or edge). Our study paves the way for defect-recognition in 3D coherent diffraction patterns for material science.},
	number = {1},
	journaltitle = {npj Computational Materials},
	author = {Lim, Bruce and Bellec, Ewen and Dupraz, Maxime and Leake, Steven and Resta, Andrea and Coati, Alessandro and Sprung, Michael and Almog, Ehud and Rabkin, Eugen and Schulli, Tobias and Richard, Marie Ingrid},
	date = {2021-12-01},
	eprinttype = {arxiv},
	eprint = {2106.16179},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\55XVISZJ\\Lim et al. - 2021 - A convolutional neural network for defect classifi.pdf:application/pdf},
}

@article{scheinker_adaptive_2020,
	title = {Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging},
	volume = {128},
	issn = {10897550},
	doi = {10.1063/5.0014725},
	abstract = {We present a novel adaptive machine-learning based approach for reconstructing three-dimensional (3D) crystals from coherent diffraction imaging. We represent the crystals using spherical harmonics ({SH}) and generate the corresponding synthetic diffraction patterns. We utilize 3D convolutional neural networks ({CNNs}) to learn a mapping between 3D diffraction volumes and the {SH}, which describe the boundary of the physical volumes from which they were generated. We use the 3D {CNN}-predicted {SH} coefficients as the initial guesses, which are then fine-tuned using adaptive model-independent feedback for improved accuracy. We also adaptively tune the locations, intensities, and decay rates of collections of radial basis functions in order to reproduce the non-uniform internal structure of 3D objects and demonstrate the method for a synthetic volume that has an internal void and a density ramp.},
	number = {18},
	journaltitle = {Journal of Applied Physics},
	author = {Scheinker, Alexander and Pokharel, Reeju},
	date = {2020-11-14},
	eprinttype = {arxiv},
	eprint = {2008.10094},
	note = {Publisher: American Institute of Physics Inc.},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\LVZJ6864\\Adaptive 3D convolutional neural network 2020.pdf:application/pdf},
}

@article{cherukara_real-time_2020,
	title = {Real-time sparse-sampled Ptychographic imaging through deep neural networks},
	url = {http://arxiv.org/abs/2004.08247},
	abstract = {Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample. A ptychographic reconstruction is achieved by means of solving a complex inverse problem that imposes constraints both on the acquisition and on the analysis of the data, which typically precludes real-time imaging due to computational cost involved in solving this inverse problem. In this work we propose {PtychoNN}, a novel approach to solve the ptychography reconstruction problem based on deep convolutional neural networks. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. The presented results demonstrate how {PtychoNN} can effectively be used on experimental data, being able to generate high quality reconstructions of a sample up to hundreds of times faster than state-of-the-art ptychography reconstruction solutions once trained. By surpassing the typical constraints of iterative model-based methods, we can significantly relax the data acquisition sampling conditions and produce equally satisfactory reconstructions. Besides drastically accelerating acquisition and analysis, this capability can enable new imaging scenarios that were not possible before, in cases of dose sensitive, dynamic and extremely voluminous samples.},
	author = {Cherukara, Mathew J. and Zhou, Tao and Nashed, Youssef and Enfedaque, Pablo and Hexemer, Alex and Harder, Ross J. and Holt, Martin V.},
	date = {2020-04-15},
	eprinttype = {arxiv},
	eprint = {2004.08247},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\35FFNCWF\\Cherukara et al. - 2020 - AI-enabled high-resolution scanning coherent diffr.pdf:application/pdf},
}

@article{stielow_reconstruction_2021,
	title = {Reconstruction of nanoscale particles from single-shot wide-angle {FEL} diffractions patterns with physics-informed neural networks},
	url = {http://arxiv.org/abs/2101.09136},
	doi = {10.1103/PhysRevE.103.053312},
	abstract = {Single-shot wide-angle diffraction imaging is a widely used method to investigate the structure of non-crystallizing objects such as nanoclusters, large proteins or even viruses. Its main advantage is that information about the three-dimensional structure of the object is already contained in a single image. This makes it useful for the reconstruction of fragile and non-reproducible particles without the need for tomographic measurements. However, currently there is no efficient numerical inversion algorithm available that is capable of determining the object's structure in real time. Neural networks, on the other hand, excel in image processing tasks suited for such purpose. Here we show how a physics-informed deep neural network can be used to reconstruct complete three-dimensional object models of uniform, convex particles on a voxel grid from single two-dimensional wide-angle scattering patterns. We demonstrate its universal reconstruction capabilities for silver nanoclusters, where the network uncovers novel geometric structures that reproduce the experimental scattering data with very high precision.},
	author = {Stielow, Thomas and Scheel, Stefan},
	date = {2021-01-22},
	eprinttype = {arxiv},
	eprint = {2101.09136},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\TI635I2V\\Stielow and Scheel - 2021 - Reconstruction of nanoscale particles from single-.pdf:application/pdf},
}

@article{wu_three-dimensional_2021,
	title = {Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks},
	volume = {7},
	issn = {20573960},
	doi = {10.1038/s41524-021-00644-z},
	abstract = {As a critical component of coherent X-ray diffraction imaging ({CDI}), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning ({ML}) model combining (optional) supervised learning with transfer learning. The trained {ML} model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed {ML} model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg {CDI} data over traditional iterative phase retrieval algorithms.},
	number = {1},
	journaltitle = {npj Computational Materials},
	author = {Wu, Longlong and Yoo, Shinjae and Suzana, Ana F. and Assefa, Tadesse A. and Diao, Jiecheng and Harder, Ross J. and Cha, Wonsuk and Robinson, Ian K.},
	date = {2021-12-01},
	eprinttype = {arxiv},
	eprint = {2103.00001},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\NCSY9YNM\\Wu et al. - 2021 - Three-dimensional coherent X-ray diffraction imaging via.pdf:application/pdf},
}

@article{yao_autophasenn_2022,
	title = {{AutoPhaseNN}: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging},
	volume = {8},
	issn = {20573960},
	doi = {10.1038/s41524-022-00803-w},
	abstract = {The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning ({DL}) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging ({BCDI}) as a representative technique, we demonstrate {AutoPhaseNN}, a {DL}-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the {DL} model during training, {AutoPhaseNN} learns to invert 3D {BCDI} data in a single shot without ever being shown real space images. Once trained, {AutoPhaseNN} can be effectively used in the 3D {BCDI} data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.},
	number = {1},
	journaltitle = {npj Computational Materials},
	author = {Yao, Yudong and Chan, Henry and Sankaranarayanan, Subramanian and Balaprakash, Prasanna and Harder, Ross J. and Cherukara, Mathew J.},
	date = {2022-12-01},
	eprinttype = {arxiv},
	eprint = {2109.14053},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\YYLLYEJW\\Yao et al. - 2022 - AutoPhaseNN unsupervised physics-aware deep learn.pdf:application/pdf},
}

@article{zhang_rapid_2019,
	title = {Rapid and robust two-dimensional phase unwrapping via deep learning},
	volume = {27},
	issn = {10944087},
	doi = {10.1364/oe.27.023173},
	abstract = {© 2019 Optical Society of America under the terms of the {OSA} Open Access Publishing Agreement Two-dimensional phase unwrapping algorithms are widely used in optical metrology and measurements. The high noise from interference measurements, however, often leads to the failure of conventional phase unwrapping algorithms. In this paper, we propose a deep convolutional neural network ({DCNN}) based method to perform rapid and robust two-dimensional phase unwrapping. In our approach, we employ a {DCNN} architecture, {DeepLabV}3+, with noise suppression and strong feature representation capabilities. The employed {DCNN} is first used to perform semantic segmentation to obtain the segmentation result of the wrapped phase map. We then combine the wrapped phase map with the segmentation result to generate the unwrapped phase. We benchmarked our results by comparing them with well-established methods. The reported approach out-performed the conventional path-dependent and path-independent algorithms. We also tested the robustness of the reported approach using interference measurements from optical metrology setups. Our results, again, clearly out-performed the conventional phase unwrap algorithms. The reported approach may find applications in optical metrology and microscopy imaging.},
	pages = {23173},
	number = {16},
	journaltitle = {Optics Express},
	author = {Zhang, Teng and Jiang, Shaowei and Zhao, Zixin and Dixit, Krishna and Zhou, Xiaofei and Hou, Jia and Zhang, Yongbing and Yan, Chenggang},
	date = {2019-08-05},
	pmid = {31510600},
	note = {Publisher: The Optical Society},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\4P3IX9G2\\Zhang et al. - 2019 - Rapid and robust two-dimensional phase unwrapping .pdf:application/pdf},
}

@article{baker_quantification_2010,
	title = {Quantification of thin film cristallographic orientation using X-ray diffraction with an area detector},
	volume = {26},
	issn = {07437463},
	doi = {10.1021/la904840q},
	abstract = {As thin films become increasingly popular (for solar cells, {LEDs}, microelectronics, batteries), quantitative morphological and crystallography information is needed to predict and optimize the film's electrical, optical, and mechanical properties. This quantification can be obtained quickly and easily with X-ray diffraction using an area detector in two sample geometries. In this paper, we describe a methodology for constructing complete pole figures for thin films with fiber texture (isotropic in-plane orientation). We demonstrate this technique on semicrystalline polymer films, self-assembled nanoparticle semiconductor films, and randomly packed metallic nanoparticle films. This method can be immediately implemented to help understand the relationship between film processing and microstructure, enabling the development of better and less expensive electronic and optoelectronic devices. © 2010 American Chemical Society.},
	pages = {9146--9151},
	number = {11},
	journaltitle = {Langmuir},
	author = {Baker, Jessy L. and Jimison, Leslie H. and Mannsfeld, Stefan and Volkman, Steven and Yin, Shong and Subramanian, Vivek and Salleo, Alberto and Alivisatos, A. Paul and Toney, Michael F.},
	date = {2010-06-01},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\BET5MIM8\\la904840q.pdf:application/pdf},
}

@inproceedings{fiddy_minimum_2003,
	title = {Minimum phase and zero distributions in 2D signals},
	volume = {5202},
	doi = {10.1117/12.505943},
	pages = {201},
	booktitle = {Optical Information Systems},
	publisher = {{SPIE}},
	author = {Fiddy, Michael A. and Shahid, Umer},
	date = {2003-11-03},
	note = {{ISSN}: 0277786X},
	keywords = {★},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\I9F6QWPY\\201.pdf:application/pdf},
}

@article{grohs_stable_2022,
	title = {Stable Gabor phase retrieval for multivariate functions},
	volume = {24},
	issn = {14359855},
	doi = {10.4171/JEMS/1114},
	abstract = {In recent work [P. Grohs and M. Rathmair, Stable Gabor phase retrieval and spectral clustering, Comm. Pure Appl. Math. (2018)] the instabilities of the Gabor phase retrieval problem, i.e., the problem of reconstructing a function f from its spectrogram {jGf} j, where (equation presented) have been completely classified in terms of the disconnectedness of the spectrogram. These findings, however, were crucially restricted to the one-dimensional case (d D 1) and therefore not relevant for many practical applications. In the present paper we not only generalize the aforementioned results to the multivariate case but also significantly improve on them. Our new results have comprehensive implications in various applications such as ptychography, a highly popular method in coherent diffraction imaging.},
	pages = {1593--1615},
	number = {5},
	journaltitle = {Journal of the European Mathematical Society},
	author = {Grohs, Philipp and Rathmair, Martin},
	date = {2022},
	eprinttype = {arxiv},
	eprint = {1903.01104},
	note = {Publisher: European Mathematical Society Publishing House},
	keywords = {Cheeger constant, Gabor transform, logarithmic derivative, Phase retrieval, stability},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\D3PLA9X7\\1927958-10.4171-jems-1114-print.pdf:application/pdf},
}

@article{grohs_injectivity_2023,
	title = {Injectivity of Gabor phase retrieval from lattice measurements},
	volume = {62},
	issn = {1096603X},
	doi = {10.1016/j.acha.2022.09.001},
	abstract = {We establish novel uniqueness results for the Gabor phase retrieval problem: if G:L2(R)→L2(R2) denotes the Gabor transform then every [Formula presented] is determined up to a global phase by the values {\textbar}Gf(x,ω){\textbar} where (x,ω) are points on the lattice b−1Z×(2c)−1Z and b{\textgreater}0 is an arbitrary positive constant. This for the first time shows that compactly-supported, complex-valued functions can be uniquely reconstructed from lattice samples of their spectrogram. Moreover, by making use of recent developments related to sampling in shift-invariant spaces by Gröchenig, Romero and Stöckler, we prove analogous uniqueness results for functions in shift-invariant spaces with Gaussian generator. Generalizations to nonuniform sampling are also presented. Finally, we compare our results to the situation where the considered signals are assumed to be real-valued.},
	pages = {173--193},
	journaltitle = {Applied and Computational Harmonic Analysis},
	author = {Grohs, Philipp and Liehr, Lukas},
	date = {2023-01-01},
	eprinttype = {arxiv},
	eprint = {2008.07238},
	note = {Publisher: Academic Press Inc.},
	keywords = {Gabor transform, Lattice measurements, Phase retrieval, Shift-invariant spaces, Spectrogram sampling},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\M9R6P7QF\\1-s2.0-S106352032200077X-main.pdf:application/pdf},
}

@article{chan_rapid_2021,
	title = {Rapid 3D nanoscale coherent imaging via physics-aware deep learning},
	volume = {8},
	issn = {19319401},
	doi = {10.1063/5.0031486},
	abstract = {Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-{CDI}-{NN}, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be "physics-aware"in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-{CDI}-{NN} can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.},
	number = {2},
	journaltitle = {Applied Physics Reviews},
	author = {Chan, Henry and Nashed, Youssef S.G. and Kandel, Saugat and Hruszkewycz, Stephan O. and Sankaranarayanan, Subramanian K.R.S. and Harder, Ross J. and Cherukara, Mathew J.},
	date = {2021-06-01},
	eprinttype = {arxiv},
	eprint = {2006.09441},
	note = {Publisher: American Institute of Physics Inc.},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\RXISS2NC\\5.0031486.pdf:application/pdf},
}

@article{ou_phase_2023,
	title = {A phase retrieval framework based on the multigrid method to alleviate the twin-image problem},
	volume = {56},
	issn = {1600-5767},
	url = {https://scripts.iucr.org/cgi-bin/paper?S1600576722010792},
	doi = {10.1107/S1600576722010792},
	abstract = {{\textless}p{\textgreater}The twin-image problem, a persistent stagnation mode in iterative projection algorithms ({IPAs}) for coherent diffraction imaging, occurs when the ideal and twin images appear simultaneously in the reconstruction. Presented here is a methodological framework for {IPAs} termed the half-cycle multigrid ({HMG}) for use in phase retrieval to alleviate the twin-image problem during the iterative process. {HMG} reconstructs the low-frequency phase first to reduce the impact of oscillation caused by phase retrieval in the higher-frequency region of Fourier space during the iteration. The higher-frequency Fourier magnitude is then added to the reconstruction stage by stage using the multigrid method. The unification of phase retrieval orientation in the low-frequency region lays the foundation for that in the whole Fourier space. The reconstruction results of simulated and experimental diffraction patterns demonstrate that {HMG} effectively reduces the probability of the twin-image problem occurring, enhances the accuracy of low-frequency information, and achieves credible and faithful reconstruction results from noisy diffraction patterns. The combination of {HMG} with the oversampling smoothness framework allows more reliable reconstruction results, proving that the {HMG} framework has good extensibility. It is expected that {HMG} can be combined with other {IPAs}.{\textless}/p{\textgreater}},
	number = {1},
	journaltitle = {Journal of Applied Crystallography},
	author = {Ou, Jiyang and Xie, Hongsheng and Zhao, Chunli and Li, Lei and Tao, Jun},
	date = {2023-02-01},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\ARUAHKSI\\Journal of Applied Crystallography - 2022 - Ou - A phase retrieval framework based on the multigrid method to alleviate the.pdf:application/pdf},
}

@article{rodriguez_oversampling_2013,
	title = {Oversampling smoothness: An effective algorithm for phase retrieval of noisy diffraction intensities},
	volume = {46},
	issn = {00218898},
	doi = {10.1107/S0021889813002471},
	abstract = {Coherent diffraction imaging ({CDI}) is high-resolution lensless microscopy that has been applied to image a wide range of specimens using synchrotron radiation, X-ray free-electron lasers, high harmonic generation, soft X-ray lasers and electrons. Despite recent rapid advances, it remains a challenge to reconstruct fine features in weakly scattering objects such as biological specimens from noisy data. Here an effective iterative algorithm, termed oversampling smoothness ({OSS}), for phase retrieval of noisy diffraction intensities is presented. {OSS} exploits the correlation information among the pixels or voxels in the region outside of a support in real space. By properly applying spatial frequency filters to the pixels or voxels outside the support at different stages of the iterative process (i.e. a smoothness constraint), {OSS} finds a balance between the hybrid input-output ({HIO}) and error reduction ({ER}) algorithms to search for a global minimum in solution space, while reducing the oscillations in the reconstruction. Both numerical simulations with Poisson noise and experimental data from a biological cell indicate that {OSS} consistently outperforms the {HIO}, {ER}-{HIO} and noise robust ({NR})-{HIO} algorithms at all noise levels in terms of accuracy and consistency of the reconstructions. It is expected that {OSS} will find application in the rapidly growing {CDI} field, as well as other disciplines where phase retrieval from noisy Fourier magnitudes is needed. The {MATLAB} (The {MathWorks} Inc., Natick, {MA}, {USA}) source code of the {OSS} algorithm is freely available from http://www.physics.ucla.edu/research/ imaging. Copyright © International Union of Crystallography 2013.},
	pages = {312--318},
	number = {2},
	journaltitle = {Journal of Applied Crystallography},
	author = {Rodriguez, Jose A. and Xu, Rui and Chen, Chien Chun and Zou, Yunfei and Miao, Jianwei},
	date = {2013-04},
	keywords = {Phase retrieval, coherent diffraction imaging, Image reconstruction, Lensless imaging, Oversampling, X-ray free-electron lasers},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\CKHELMVH\\OSS_JAC_2013.pdf:application/pdf},
}

@article{carbone_when_2022,
	title = {When not to use machine learning: a perspective on potential and limitations},
	url = {http://arxiv.org/abs/2210.02666},
	abstract = {The unparalleled success of artificial intelligence ({AI}) in the technology sector has catalyzed an enormous amount of research in the scientific community. It has proven to be a powerful tool, but as with any rapidly developing field, the deluge of information can be overwhelming, confusing and sometimes misleading. This can make it easy to become lost in the same hype cycles that have historically ended in the periods of scarce funding and depleted expectations known as {AI} Winters. Furthermore, while the importance of innovative, high-risk research cannot be overstated, it is also imperative to understand the fundamental limits of available techniques, especially in young fields where the rules appear to be constantly rewritten and as the likelihood of application to high-stakes scenarios increases. In this perspective, we highlight the guiding principles of data-driven modeling, how these principles imbue models with almost magical predictive power, and how they also impose limitations on the scope of problems they can address. Particularly, understanding when not to use data-driven techniques, such as machine learning, is not something commonly explored, but is just as important as knowing how to apply the techniques properly. We hope that the discussion to follow provides researchers throughout the sciences with a better understanding of when said techniques are appropriate, the pitfalls to watch for, and most importantly, the confidence to leverage the power they can provide.},
	author = {Carbone, M. R.},
	date = {2022-10-06},
	eprinttype = {arxiv},
	eprint = {2210.02666},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\523E4QMW\\2210.02666.pdf:application/pdf},
}

@article{kogan_2d_2017,
	title = {On the 2D Phase Retrieval Problem},
	volume = {65},
	issn = {1053587X},
	doi = {10.1109/TSP.2016.2631455},
	abstract = {The recovery of a signal from the magnitude of its Fourier transform, also known as phase retrieval, is of fundamental importance in many scientific fields. It is well known that due to the loss of Fourier phase the problem in one-dimensional (1D) is ill-posed. Without further constraints, there is no unique solution to the problem. In contrast, uniqueness up to trivial ambiguities very often exists in higher dimensions, with mild constraints on the input. In this paper, we focus on the 2D phase retrieval problem and provide insight into this uniqueness property by exploring the connection between the 2D and 1D formulations. In particular, we show that 2D phase retrieval can be cast as a 1D problem with additional constraints, which limit the solution space. We then prove that only one additional constraint is sufficient to reduce the many feasible solutions in the 1D setting to a unique solution for almost all signals. These results allow to obtain an analytical approach (with combinatorial complexity) to solve the 2D phase retrieval problem when it is unique.},
	pages = {1058--1067},
	number = {4},
	journaltitle = {{IEEE} Transactions on Signal Processing},
	author = {Kogan, Dani and Eldar, Yonina C. and Oron, Dan},
	date = {2017-02-15},
	eprinttype = {arxiv},
	eprint = {1605.08487},
	note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
	keywords = {Phase retrieval, 2D autocorrelation, Uniqueness},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\2J9AXBG5\\On_the_2D_Phase_Retrieval_Problem.pdf:application/pdf},
}

@article{lehtinen_noise2noise_2018,
	title = {Noise2Noise: Learning Image Restoration without Clean Data},
	url = {http://arxiv.org/abs/1803.04189},
	abstract = {We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled {MRI} scans -- all corrupted by different processes -- based on noisy data only.},
	author = {Lehtinen, Jaakko and Munkberg, Jacob and Hasselgren, Jon and Laine, Samuli and Karras, Tero and Aittala, Miika and Aila, Timo},
	date = {2018-03-12},
	eprinttype = {arxiv},
	eprint = {1803.04189},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\VR53SEBK\\1803.04189.pdf:application/pdf},
}

@article{boulle_convolutional_2023,
	title = {Convolutional neural network analysis of x-ray diffraction data: strain profile retrieval in ion beam modified materials},
	volume = {4},
	issn = {2632-2153},
	url = {https://iopscience.iop.org/article/10.1088/2632-2153/acab4c},
	doi = {10.1088/2632-2153/acab4c},
	abstract = {{\textless}p{\textgreater} This work describes a proof of concept demonstrating that convolutional neural networks ({CNNs}) can be used to invert x-ray diffraction ({XRD}) data, so as to, for instance, retrieve depth-resolved strain profiles. The determination of strain distributions in disordered materials is critical in several technological domains, such as the semiconductor industry for instance. Using numerically generated data, a dedicated {CNN} has been developed, optimized, and trained, with the ultimate objective of inferring spatial strain profiles on the sole basis of {XRD} data, without the need of {\textless}italic{\textgreater}a priori{\textless}/italic{\textgreater} knowledge or human intervention. With the example {ZrO} $_{\textrm{2}}$ single crystals, in which atomic disorder and strain are introduced by means of ion irradiation, we investigate the physical parameters of the disordered material that condition the performances of the {CNN}. Simple descriptors of the strain distribution, such as the maximum strain and the strained depth, are predicted with accuracies of 94\% and 91\%, respectively. The exact shape of the strain distribution is predicted with a 82\% accuracy, and 76\% for strain levels \<2\% where the amount of meaningful information in the {XRD} data is significantly decreased. The robustness of the {CNN} against the number of predicted parameters and the size of the training dataset, as well as the uniqueness of the solution in some challenging cases, are critically discussed. Finally, the potential of the {CNN} has been tested on real, experimental, data. Interestingly, while the {CNN} has not been trained to operate on experimental data, it still shows promising performances with predictions achieved in a few seconds and corresponding root-mean-square errors in the 0.12–0.17 range for a fully automated approach, {\textless}italic{\textgreater}vs.{\textless}/italic{\textgreater} a 0.06–0.12 range for a classical, human-based, approach that, in turn, requires several tens of minutes to optimize the solution. While the overall accuracy of the {CNN} has to be improved, these results pave the way for a fully automated {XRD} data analysis. {\textless}/p{\textgreater}},
	pages = {015002},
	number = {1},
	journaltitle = {Machine Learning: Science and Technology},
	author = {Boulle, A and Debelle, A},
	date = {2023-03-01},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\UI6ST8PI\\Boulle_2023_Mach._Learn. _Sci._Technol._4_015002.pdf:application/pdf},
}

@article{dong_phase_2023,
	title = {Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial},
	volume = {40},
	issn = {1053-5888},
	url = {https://ieeexplore.ieee.org/document/10004797/},
	doi = {10.1109/MSP.2022.3219240},
	pages = {45--57},
	number = {1},
	journaltitle = {{IEEE} Signal Processing Magazine},
	author = {Dong, Jonathan and Valzania, Lorenzo and Maillard, Antoine and Pham, Thanh-an and Gigan, Sylvain and Unser, Michael},
	date = {2023-01},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\DZAGZVE4\\Phase_Retrieval_From_Computational_Imaging_to_Machine_Learning_A_tutorial.pdf:application/pdf},
}

@article{xiang_deep_2023,
	title = {Deep learning for image inpainting: A survey},
	volume = {134},
	issn = {00313203},
	doi = {10.1016/j.patcog.2022.109046},
	abstract = {Image inpainting has been widely exploited in the field of computer vision and image processing. The main purpose of image inpainting is to produce visually plausible structure and texture for the missing regions of damaged images. In the past decade, the success of deep learning has brought new opportunities to many vision tasks, which promoted the development of a large number of deep learning-based image inpainting methods. Although these methods have many similarities, they also have their own characteristics due to the differences in data types, application scenarios, computing platforms, etc. It is necessary to classify and summarize these methods to provide a reference for the research community. In this survey, we present a comprehensive overview of recent advances in deep learning-based image inpainting. First, we categorize the deep learning-based techniques from multiple perspectives: inpainting strategies, network structures, and loss functions. Second, we summarize the open source codes and representative public datasets, and introduce the evaluation metrics for quantitative comparisons. Third, we summarize the real-world applications of image inpainting in different scenarios, and give a detailed analysis on the performance of different inpainting algorithms. At last, we conclude the survey and discuss about the future directions.},
	journaltitle = {Pattern Recognition},
	author = {Xiang, Hanyu and Zou, Qin and Nawaz, Muhammad Ali and Huang, Xianfeng and Zhang, Fan and Yu, Hongkai},
	date = {2023-02-01},
	note = {Publisher: Elsevier Ltd},
	keywords = {Convolutional neural network, Generative adversarial network, Image inpainting, Image restoration},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\RV6XEEHQ\\1-s2.0-S003132032200526X-main.pdf:application/pdf},
}

@article{chavez_comparison_2022,
	title = {A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering},
	volume = {55},
	issn = {1600-5767},
	url = {https://scripts.iucr.org/cgi-bin/paper?S1600576722007105},
	doi = {10.1107/S1600576722007105},
	abstract = {{\textless}p{\textgreater}The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.{\textless}/p{\textgreater}},
	pages = {1277--1288},
	number = {5},
	journaltitle = {Journal of Applied Crystallography},
	author = {Chavez, Tanny and Roberts, Eric J. and Zwart, Petrus H. and Hexemer, Alexander},
	date = {2022-10-01},
	note = {Publisher: International Union of Crystallography ({IUCr})},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\GN4PHLIH\\jl5040.pdf:application/pdf},
}

@article{wang_image_2004,
	title = {Image quality assessment: From error visibility to structural similarity},
	volume = {13},
	issn = {10577149},
	doi = {10.1109/TIP.2003.819861},
	abstract = {Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with {JPEG} and {JPEG}2000.},
	pages = {600--612},
	number = {4},
	journaltitle = {{IEEE} Transactions on Image Processing},
	author = {Wang, Zhou and Bovik, Alan Conrad and Sheikh, Hamid Rahim and Simoncelli, Eero P.},
	date = {2004-04},
	pmid = {15376593},
	keywords = {Error sensitivity, Human visual system ({HVS}), Image coding, Image quality assessment, {JPEG}, {JPEG}2000, Perceptual quality, Structural information, Structural similarity ({SSIM})},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\CRJBC668\\Image_quality_assessment_from_error_visibility_to_structural_similarity.pdf:application/pdf},
}

@article{carnis_towards_2019,
	title = {Towards a quantitative determination of strain in Bragg Coherent X-ray Diffraction Imaging: artefacts and sign convention in reconstructions},
	volume = {9},
	issn = {20452322},
	doi = {10.1038/s41598-019-53774-2},
	abstract = {Bragg coherent X-ray diffraction imaging ({BCDI}) has emerged as a powerful technique to image the local displacement field and strain in nanocrystals, in three dimensions with nanometric spatial resolution. However, {BCDI} relies on both dataset collection and phase retrieval algorithms that can induce artefacts in the reconstruction. Phase retrieval algorithms are based on the fast Fourier transform ({FFT}). We demonstrate how to calculate the displacement field inside a nanocrystal from its reconstructed phase depending on the mathematical convention used for the {FFT}. We use numerical simulations to quantify the influence of experimentally unavoidable detector deficiencies such as blind areas or limited dynamic range as well as post-processing filtering on the reconstruction. We also propose a criterion for the isosurface determination of the object, based on the histogram of the reconstructed modulus. Finally, we study the capability of the phasing algorithm to quantitatively retrieve the surface strain (i.e., the strain of the surface voxels). This work emphasizes many aspects that have been neglected so far in {BCDI}, which need to be understood for a quantitative analysis of displacement and strain based on this technique. It concludes with the optimization of experimental parameters to improve throughput and to establish {BCDI} as a reliable 3D nano-imaging technique.},
	number = {1},
	journaltitle = {Scientific Reports},
	author = {Carnis, Jérôme and Gao, Lu and Labat, Stéphane and Kim, Young Yong and Hofmann, Jan P. and Leake, Steven J. and Schülli, Tobias U. and Hensen, Emiel J.M. and Thomas, Olivier and Richard, Marie Ingrid},
	date = {2019-12-01},
	pmid = {31758040},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\DWIVSXTP\\s41598-019-53774-2.pdf:application/pdf},
}

@article{butola_robust_2022,
	title = {Robust Phase Retrieval with Complexity-Guidance for Coherent X-Ray Imaging},
	volume = {2022},
	doi = {10.34133/2022/9819716},
	abstract = {Reconstruction of a stable and reliable solution from noisy and incomplete Fourier intensity data is a challenging problem for iterative phase retrieval algorithms. The typical methodology employed in the coherent X-ray imaging ({CXI}) literature involves thousands of iterations of well-known phase retrieval algorithms, e.g., hybrid input-output ({HIO}) or relaxed averaged alternating reflections ({RAAR}), that are concluded with a smaller number of error reduction ({ER}) iterations. Since the single run of this methodology may not provide a reliable solution, hundreds of trial solutions are first obtained by initializing the phase retrieval algorithm with independent random guesses. The resulting trial solutions are then averaged with appropriate phase adjustment, and resolution of the averaged reconstruction is assessed by plotting the phase retrieval transfer function ({PRTF}). In this work, we examine this commonly used {RAAR}-{ER} methodology from the perspective of the complexity parameter introduced by us in recent years. It is observed that the single run of the {RAAR}-{ER} algorithm provides a solution with undesirable grainy artifacts that persist to some extent even after averaging the multiple trial solutions. The grainy features are spurious in the sense that they are smaller in size compared to the resolution predicted by the {PRTF} curve. This inconsistency can be addressed by a novel methodology that we refer to as complexity-guided {RAAR} ({CG}-{RAAR}). The methodology is demonstrated with simulations and experimental data sets from the {CXIDB} database. In addition to providing consistent solution, {CG}-{RAAR} is also observed to require reduced number of independent trials for averaging.},
	journaltitle = {Intelligent Computing},
	author = {Butola, Mansi and Rajora, Sunaina and Khare, Kedar},
	date = {2022-01},
	note = {Publisher: American Association for the Advancement of Science ({AAAS})},
	file = {2022_9819716.pdf:C\:\\Users\\matteo1996a\\Zotero\\storage\\BE4X8AUQ\\2022_9819716.pdf:application/pdf;9819716.pdf:C\:\\Users\\matteo1996a\\Zotero\\storage\\SVH3B6RI\\9819716.pdf:application/pdf},
}

@article{bellisario_noise_2022,
	title = {Noise reduction and mask removal neural network for X-ray single-particle imaging},
	volume = {55},
	issn = {16005767},
	doi = {10.1107/S1600576721012371},
	abstract = {Free-electron lasers could enable X-ray imaging of single biological macromolecules and the study of protein dynamics, paving the way for a powerful new imaging tool in structural biology, but a low signal-to-noise ratio and missing regions in the detectors, colloquially termed 'masks', affect data collection and hamper real-time evaluation of experimental data. In this article, the challenges posed by noise and masks are tackled by introducing a neural network pipeline that aims to restore diffraction intensities. For training and testing of the model, a data set of diffraction patterns was simulated from 10 900 different proteins with molecular weights within the range of 10-100 {kDa} and collected at a photon energy of 8 {keV}. The method is compared with a simple low-pass filtering algorithm based on autocorrelation constraints. The results show an improvement in the mean-squared error of roughly two orders of magnitude in the presence of masks compared with the noisy data. The algorithm was also tested at increasing mask width, leading to the conclusion that demasking can achieve good results when the mask is smaller than half of the central speckle of the pattern. The results highlight the competitiveness of this model for data processing and the feasibility of restoring diffraction intensities from unknown structures in real time using deep learning methods. Finally, an example is shown of this preprocessing making orientation recovery more reliable, especially for data sets containing very few patterns, using the expansion-maximization-compression algorithm.},
	pages = {122--132},
	journaltitle = {Journal of Applied Crystallography},
	author = {Bellisario, Alfredo and Maia, Filipe R.N.C. and Ekeberg, Tomas},
	date = {2022-02-01},
	note = {Publisher: International Union of Crystallography},
	keywords = {Coherent X-ray diffractive imaging ({CXDI}), Diffract-then-destroy, Free-electron lasers, Imaging, Protein structures, Single Particles, {XFELs}},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\ICE6IG4H\\Bellisario et al. - 2022 - Noise reduction and mask removal neural network fo.pdf:application/pdf},
}

@article{dong_image_2016,
	title = {Image Super-Resolution Using Deep Convolutional Networks},
	volume = {38},
	issn = {01628828},
	doi = {10.1109/TPAMI.2015.2439281},
	abstract = {We propose a deep learning method for single image super-resolution ({SR}). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network ({CNN}) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based {SR} methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep {CNN} has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.},
	pages = {295--307},
	number = {2},
	journaltitle = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
	author = {Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
	date = {2016-02-01},
	pmid = {26761735},
	eprinttype = {arxiv},
	eprint = {1501.00092},
	note = {Publisher: {IEEE} Computer Society},
	keywords = {deep convolutional neural networks, sparse coding, Super-resolution},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\UNRWYIEU\\Image_Super-Resolution_Using_Deep_Convolutional_Networks.pdf:application/pdf},
}

@article{zhou_digital_2020,
	title = {Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning},
	volume = {3},
	issn = {25787519},
	doi = {10.1364/osac.393325},
	abstract = {Optical coherence tomography angiography ({OCTA}) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes digital resolution degradation, such as the angiograms in wide-field {OCTA}. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 × 3 mm 2 field of view ({FOV}) of the 8 × 8 mm 2 foveal {OCTA} images (a sampling density of 22.9 µ m) to the native 3 × 3 mm 2 en face {OCTA} images (a sampling density of 12.2 µ m). We employed a cycle-consistent adversarial network architecture in this conversion. The quantitative analysis using the perceptual similarity measures shows the generated {OCTA} images are closer to the native 3 × 3 mm 2 scans. Besides, the results show the proposed method could also enhance the signal-to-noise ratio. We further applied our method to enhance diseased cases and calculate vascular biomarkers, which demonstrates its generalization performance and clinical perspective.},
	pages = {1664},
	number = {6},
	journaltitle = {{OSA} Continuum},
	author = {Zhou, Ting and Yang, Jianlong and Zhou, Kang and Fang, Liyang and Hu, Yan and Cheng, Jun and Zhao, Yitian and Chen, Xiangping and Gao, Shenghua and Liu, Jiang},
	date = {2020-06-15},
	eprinttype = {arxiv},
	eprint = {1910.01344},
	note = {Publisher: Optica Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\LJUCPLJ7\\osac-3-6-1664.pdf:application/pdf},
}

@article{miao_coherent_2012,
	title = {Coherent x-ray diffraction imaging},
	volume = {18},
	issn = {1077260X},
	doi = {10.1109/JSTQE.2011.2157306},
	abstract = {For centuries, lens-based microscopy, such as optical, phase-contrast, fluorescence, confocal, and electron microscopy, has played an important role in the evolution of modern science and technology. In 1999, a novel form of microscopy, i.e., coherent diffraction imaging (also termed coherent diffraction microscopy or lensless imaging), was developed and transformed our conventional view of microscopy, in which the diffraction pattern of a noncrystalline specimen or a nanocrystal was first measured and then directly phased to obtain a high-resolution image. The well-known phase problem was solved by combining the oversampling method with iterative algorithms. In this paper, we will briefly discuss the principle of coherent diffraction imaging, present various implementation schemes of this imaging modality, and illustrate its broad applications in materials science, nanoscience, and biology. As coherent X-ray sources such as high harmonic generation and X-ray free-electron lasers are presently under rapid development worldwide, coherent diffraction imaging can potentially be applied to perform high-resolution imaging of materials/nanoscience and biological specimens at the femtosecond time scale. © 2006 {IEEE}.},
	pages = {399--410},
	number = {1},
	journaltitle = {{IEEE} Journal on Selected Topics in Quantum Electronics},
	author = {Miao, Jianwei and Sandberg, Richard L. and Song, Changyong},
	date = {2012},
	keywords = {phase retrieval, Ankylography, coherent diffraction imaging ({CDI}), equally sloped tomography ({EST}), high harmonic generation ({HHG}), lensless imaging, oversampling, X-ray free-electron lasers ({XFEL})},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\BFQJJQRN\\Coherent_X-Ray_Diffraction_Imaging.pdf:application/pdf},
}

@report{berthomme_how_nodate,
	title = {How to use Information Theory for Image Inpainting and Blind Spot Filling-in?},
	url = {http://wwwlasmea.univ-bpclermont.fr/},
	abstract = {This paper shows how information theory can both drive the digital image inpainting process and the optical illusion due to the blind spot. The defended position is that the missing information is padded by the "most probable information around" via a simple filling-in scheme. Thus the proposed algorithm aims to keep the entropy constant. It cares not to create too much novelty as well as not to destroy too much information. For this, the image is broken down into regular squares in order to build a dictionary of unique words and to estimate the entropy. Then the occluded region is completed, word by word and layer by layer, by picking the element which respects the existing image, which minimizes the entropy deviation if there are several candidates, and which limits its potential increase in the case where no compatible word exists and where a new one must be introduced.},
	author = {Berthommé, Jean-Marc and Chateau, Thierry and Dhome, Michel},
	keywords = {Blind Spot, Image Inpainting, Information Theory},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\UDJMISM3\\Berthomme_2013_VISAPP.pdf:application/pdf},
}

@article{giewekemeyer_experimental_2019,
	title = {Experimental 3D coherent diffractive imaging from photon-sparse random projections},
	volume = {6},
	issn = {20522525},
	doi = {10.1107/S2052252519002781},
	abstract = {The routine atomic resolution structure determination of single particles is expected to have profound implications for probing structure-function relationships in systems ranging from energy-storage materials to biological molecules. Extremely bright ultrashort-pulse X-ray sources - X-ray free-electron lasers ({XFELs}) - provide X-rays that can be used to probe ensembles of nearly identical nanoscale particles. When combined with coherent diffractive imaging, these objects can be imaged; however, as the resolution of the images approaches the atomic scale, the measured data are increasingly difficult to obtain and, during an X-ray pulse, the number of photons incident on the 2D detector is much smaller than the number of pixels. This latter concern, the signal 'sparsity', materially impedes the application of the method. An experimental analog using a conventional X-ray source is demonstrated and yields signal levels comparable with those expected from single biomolecules illuminated by focused {XFEL} pulses. The analog experiment provides an invaluable cross check on the fidelity of the reconstructed data that is not available during {XFEL} experiments. Using these experimental data, it is established that a sparsity of order 1.3 × 10 -3 photons per pixel per frame can be overcome, lending vital insight to the solution of the atomic resolution {XFEL} single-particle imaging problem by experimentally demonstrating 3D coherent diffractive imaging from photon-sparse random projections.},
	pages = {357--365},
	journaltitle = {{IUCrJ}},
	author = {Giewekemeyer, K. and Aquila, A. and Loh, N. T.D. and Chushkin, Y. and Shanks, K. S. and Weiss, J. T. and Tate, M. W. and Philipp, H. T. and Stern, S. and Vagovic, P. and Mehrjoo, M. and Teo, C. and Barthelmess, M. and Zontone, F. and Chang, C. and Tiberio, R. C. and Sakdinawat, A. and Williams, G. J. and Gruner, S. M. and Mancuso, A. P.},
	date = {2019},
	eprinttype = {arxiv},
	eprint = {1811.05883},
	note = {Publisher: International Union of Crystallography},
	keywords = {X-ray free-electron lasers, {XFELs}, coherent X-ray diffractive imaging ({CXDI}), phase problem, single particles},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\3MMGKRCQ\\it5020.pdf:application/pdf},
}

@article{shi_deep_2020,
	title = {Deep prior-based sparse representation model for diffraction imaging: A plug-and-play method},
	volume = {168},
	issn = {01651684},
	doi = {10.1016/j.sigpro.2019.107350},
	abstract = {Diffraction imaging problem, i.e. recovery of a high-resolution or high-quality image from the intensity diffraction pattern, arises in many science and engineering fields. Recent efforts to solve this problem are exploiting sparse representation techniques. However, existing sparse representation models cannot explore inherent priors of the images to be reconstructed sufficiently. Imaging algorithms employed such traditional sparse representation models often suffer from low-quality reconstructions in the case of the noise or low-resolution observation. To address this issue, we propose a deep prior-based sparse representation ({DPSR}) regularization model that can impose the sparsity and the deep prior on the unknown image. The {DPSR} model is a plug-and-play model, namely that one can plug any effective deep denoiser into this model. We apply this model to coded diffraction imaging. To perform high-resolution imaging, a sub-pixel resolution coded diffraction pattern observation model is proposed. Based on this observation model, a diffraction imaging optimization problem is formulated. The formulated optimization problem is tackled by using the alternating optimization strategy and the epigraph concept. Compared to the benchmark diffraction imaging algorithms, the proposed algorithm has a notable peak signal-to-noise ratio ({PSNR}) gain of about 2 {dB}. Meanwhile, the proposed algorithm can perform high-resolution diffraction imaging with the pixel super-resolution factor of 4 under the single observation case. A demo code of the proposed algorithm is available at https://github.com/shibaoshun/{DPSR}.},
	journaltitle = {Signal Processing},
	author = {Shi, Baoshun and Lian, Qiusheng and Chang, Huibin},
	date = {2020-03-01},
	note = {Publisher: Elsevier B.V.},
	keywords = {Deep learning, Diffraction imaging, Epigraph, Plug-and-play, Sparse representation},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\C6RRM75K\\1-s2.0-S0165168419304037-main.pdf:application/pdf},
}

@article{yokoyama_sparse_2019,
	title = {Sparse phase retrieval algorithm for observing isolated magnetic skyrmions by coherent soft X-ray diffraction imaging},
	volume = {88},
	issn = {13474073},
	doi = {10.7566/JPSJ.88.024009},
	abstract = {A magnetic skyrmion, a topological magnetic structure on nanometric spatial scale, behaves like an isolated particle in a magnetic material. Investigating the response of a skyrmion to an applied external field in real space is effective for understanding its characteristics. Such observations require a real-space imaging technique with high temporal and spatial resolution. Coherent X-ray diffraction imaging is a powerful technique for observing magnetic skyrmions that takes advantage of a short X-ray pulse and short wavelength. However, the magnetic scattering intensity from isolated magnetic skyrmions is weaker than that of the magnetic skyrmion lattice and thus is susceptible to noise. In this study, we propose a sparse phase retrieval algorithm ({SpPRA}), which involves an iterative Fourier transform and sparse modeling, and we demonstrated that it can retrieve the phase from diffraction data including noise and missing information. Considering the sparseness of the magnetic distribution as regularization enables preventing overfitting the noise and predicting the diffraction data of the missing region.},
	number = {2},
	journaltitle = {Journal of the Physical Society of Japan},
	author = {Yokoyama, Yuichi and Arima, Taka hisa and Okada, Masato and Yamasaki, Yuichi},
	date = {2019},
	note = {Publisher: Physical Society of Japan},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\LW4LD45Y\\jpsj.88.024009.pdf:application/pdf},
}

@article{maddali_sparse_2018,
	title = {Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies},
	volume = {8},
	issn = {20452322},
	doi = {10.1038/s41598-018-23040-y},
	abstract = {Coherent X-ray photons with energies higher than 50 {keV} offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this data set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging ({BCDI}) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a {BCDI} data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. We use simulated {BCDI} data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.},
	number = {1},
	journaltitle = {Scientific Reports},
	author = {Maddali, S. and Calvo-Almazan, I. and Almer, J. and Kenesei, P. and Park, J. S. and Harder, R. and Nashed, Y. and Hruszkewycz, S. O.},
	date = {2018-12-01},
	pmid = {29563508},
	eprinttype = {arxiv},
	eprint = {1712.01108},
	note = {Publisher: Nature Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\QAXGNIR5\\41598_2018_Article_23040.pdf:application/pdf},
}

@article{favre-nicolin_pynx_2020,
	title = {{PyNX}: High-performance computing toolkit for coherent X-ray imaging based on operators},
	volume = {53},
	issn = {16005767},
	doi = {10.1107/S1600576720010985},
	abstract = {The open-source {PyNX} toolkit has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units ({GPUs}) to achieve high-performance computing speeds. The toolkit can be used for coherent diffraction imaging ({CDI}), ptychography and wavefront propagation, in the far- or near-field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles...) can be implemented in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple {GPUs}, e.g. for large ptychography data sets. Command-line scripts are available for on-line {CDI} and ptychography analysis, either from raw beamline data sets or using the coherent X-ray imaging data format.},
	pages = {1404--1413},
	journaltitle = {Journal of Applied Crystallography},
	author = {Favre-Nicolin, Vincent and Girard, Gaétan and Leake, Steven and Carnis, Jerome and Chushkin, Yuriy and Kieffer, Jerome and Paleo, Pierre and Richard, Marie Ingrid},
	date = {2020-10-01},
	eprinttype = {arxiv},
	eprint = {2008.11511},
	note = {Publisher: International Union of Crystallography},
	keywords = {Coherent diffraction imaging, Coherent X-ray imaging, Graphical processing units, Ptychography},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\RHTRIJ7G\\Favre-Nicolin et al. - 2020 - PyNX high-performance computing toolkit for coher.pdf:application/pdf},
}

@article{ronneberger_u-net_2015,
	title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	url = {http://arxiv.org/abs/1505.04597},
	abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the {ISBI} challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and {DIC}) we won the {ISBI} cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent {GPU}. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	date = {2015-05-18},
	eprinttype = {arxiv},
	eprint = {1505.04597},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\DXY2F43D\\UNetPaper.pdf:application/pdf},
}

@article{li_visualizing_2017,
	title = {Visualizing the Loss Landscape of Neural Nets},
	url = {http://arxiv.org/abs/1712.09913},
	abstract = {Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effects on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.},
	author = {Li, Hao and Xu, Zheng and Taylor, Gavin and Studer, Christoph and Goldstein, Tom},
	date = {2017-12-28},
	eprinttype = {arxiv},
	eprint = {1712.09913},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\HN4P5WIA\\skip connections.pdf:application/pdf},
}

@inproceedings{ponchut_maxipix_2011,
	title = {{MAXIPIX}, a fast readout photon-counting X-ray area detector for synchrotron applications},
	volume = {6},
	doi = {10.1088/1748-0221/6/01/C01069},
	abstract = {A 2D photon-counting X-ray detector system with 1.4 {kHz} frame rate and 55 μm spatial resolution has been developed and commissionned on {ESRF} beamlines. The system called {MAXIPIX} (Multichip Area X-ray detector based on a photon-counting {PIXel} array) consists of a detector module implementing up to five {MEDIPIX}-2 or {TIMEPIX} photon-counting readout chips, a custom readout interface board and a Linux acquisition workstation. The detector module readout time is 290 microseconds, allowing the system to achieve sustained frame rates of 280 Hz to 1400 Hz depending on the number of connected chips. An effective time resolution of 60 ns was measured using the {ESRF} pulsed modes and a {TIMEPIX} module. The system architecture and characteristics are presented, as well as a summary of its applications on {ESRF} beamlines. © 2011 {IOP} Publishing Ltd and {SISSA}.},
	booktitle = {Journal of Instrumentation},
	author = {Ponchut, C. and Rigal, J. M. and Clément, J. and Papillon, E. and Homs, A. and Petitdemange, S.},
	date = {2011-01},
	note = {Issue: 1
{ISSN}: 17480221},
	keywords = {Hybrid detectors, Pixelated detectors and associated {VLSI} electronics, X-ray detectors, X-ray diffraction detectors},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\G7LP57RI\\C_Ponchut_2011_J._Inst._6_C01069.pdf:application/pdf},
}

@article{jam_comprehensive_2021,
	title = {A comprehensive review of past and present image inpainting methods},
	volume = {203},
	issn = {1090235X},
	doi = {10.1016/j.cviu.2020.103147},
	abstract = {Images can be described as visual representations or likeness of something (person or object) which can be reproduced or captured, e.g. a hand drawing, photographic material. However, for images on photographic material, images can have defects at the point of captured, become damaged, or degrade over time. Historically, these were restored by hand to maintain image quality using a process known as inpainting. The advent of the digital age has seen the rapid shift image storage technologies, from hard-copies to digitalised units in a less burdensome manner with the application of digital tools. This paper presents a comprehensive review of image inpainting methods over the past decade and the commonly used performance metrics and datasets. To increase the clarity of our review, we use a hierarchical representation for the past state-of-the-art traditional methods and the present state-of-the-art deep learning methods. For traditional methods, we divide the techniques into five sub-categories, i.e. Exemplar-based texture synthesis, Exemplar-based structure synthesis, Diffusion-based methods, Sparse representation methods and Hybrid methods. Then we review the deep learning methods, i.e. Convolutional Neural Networks and Generative Adversarial Networks. We detail the strengths and weaknesses of each to provide new insights in the field. To address the challenges raised from our findings, we outline some potential future works.},
	journaltitle = {Computer Vision and Image Understanding},
	author = {Jam, Jireh and Kendrick, Connah and Walker, Kevin and Drouard, Vincent and Hsu, Jison Gee Sern and Yap, Moi Hoon},
	date = {2021-02-01},
	note = {Publisher: Academic Press Inc.},
	keywords = {Convolutional neural network, Image inpainting, Generative adversarial networks, Restoration, Texture synthesis},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\AD3IFV9Q\\review inpainting.pdf:application/pdf},
}

@inproceedings{estrada_paineira_2022,
	title = {{PAINEIRA} beamline at Sirius: An automated facility for polycrystalline {XRD} characterization},
	volume = {2380},
	doi = {10.1088/1742-6596/2380/1/012033},
	abstract = {Herein we outline the design of the Paineira beamline - a beamline dedicated to powder X-ray diffraction at the new Brazilian synchrotron source (Sirius). The optical layout optimizes the high photon brightness of the fourth-generation synchrotron light source generated by an undulator. The heavy-duty diffractometer operating in Debye-Scherrer geometry has two detector setups: a multi-analyser crystal for high-resolution and an arc-shaped area detector covering 100° in scattering angle (2θ). The beamline is optimized by carrying out high-throughput measurements with the possibility of rapid setup changes to in-situ and operando measurement conditions. Paineira beamline is under construction and will open new opportunities for materials science, catalysis, energy materials, and geoscience research with synchrotron {XRD} in Brazil.},
	booktitle = {Journal of Physics: Conference Series},
	publisher = {Institute of Physics},
	author = {Estrada, F. R. and Barrett, D. H. and Ferreira, A. I. and Mauricio, J. C. and Rigamonti, H. and Meyer, B. C. and Tolentino, H. C.N. and Westfahl, H. and Rodella, C. B.},
	date = {2022},
	note = {Issue: 1
{ISSN}: 17426596},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\7UIQF6XD\\PimegaDetector.pdf:application/pdf},
}

@book{bayat_proceedings_nodate,
	title = {Proceedings of 2017 International Conference on Engineering \& Technology ({ICET}'2017) : Akdeniz University, Antalya, Turkey, 21-23 August, 2017},
	isbn = {978-1-5386-1949-0},
	abstract = {"Part Number: {CFP}1716U-{ART}."},
	author = {Bayat, Oguz and Aljawarneh, Shadi and Carlak, Hamza Feza and {International Association of Researchers} and {Institute of Electrical and Electronics Engineers} and {Akdeniz Üniversitesi}},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\R2L66NUY\\Understanding_of_a_convolutional_neural_network.pdf:application/pdf},
}

@article{chen_rethinking_2017,
	title = {Rethinking Atrous Convolution for Semantic Image Segmentation},
	url = {http://arxiv.org/abs/1706.05587},
	abstract = {In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `{DeepLabv}3' system significantly improves over our previous {DeepLab} versions without {DenseCRF} post-processing and attains comparable performance with other state-of-art models on the {PASCAL} {VOC} 2012 semantic image segmentation benchmark.},
	author = {Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig},
	date = {2017-06-17},
	eprinttype = {arxiv},
	eprint = {1706.05587},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\6CRQVQYC\\CNN.pdf:application/pdf},
}

@article{marchesini_unified_2007,
	title = {A unified evaluation of iterative projection algorithms for phase retrieval},
	volume = {78},
	issn = {00346748},
	doi = {10.1063/1.2403783},
	abstract = {Iterative projection algorithms are successfully being used as a substitute of lenses to recombine, numerically rather than optically, light scattered by illuminated objects. Images obtained computationally allow aberration-free diffraction-limited imaging and the possibility of using radiation for which no lenses exist. The challenge of this imaging technique is transferred from the lenses to the algorithms. We evaluate these new computational "instruments" developed for the phase-retrieval problem, and discuss acceleration strategies. © 2007 American Institute of Physics.},
	number = {1},
	journaltitle = {Review of Scientific Instruments},
	author = {Marchesini, S.},
	date = {2007},
	pmid = {17503899},
	eprinttype = {arxiv},
	eprint = {physics/0603201},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\VTN3JMVB\\Marchesini - 2007 - A unified evaluation of iterative projection algor.pdf:application/pdf},
}

@article{clark_three-dimensional_2015,
	title = {Three-dimensional imaging of dislocation propagation during crystal growth and dissolution},
	volume = {14},
	issn = {14764660},
	doi = {10.1038/nmat4320},
	abstract = {Atomic-level defects such as dislocations play key roles in determining the macroscopic properties of crystalline materials. Their effects range from increased chemical reactivity to enhanced mechanical properties. Dislocations have been widely studied using traditional techniques such as X-ray diffraction and optical imaging. Recent advances have enabled atomic force microscopy to study single dislocations in two dimensions, while transmission electron microscopy ({TEM}) can now visualize strain fields in three dimensions with near-atomic resolution. However, these techniques cannot offer three-dimensional imaging of the formation or movement of dislocations during dynamic processes. Here, we describe how Bragg coherent diffraction imaging ({BCDI}; refs,) can be used to visualize in three dimensions, the entire network of dislocations present within an individual calcite crystal during repeated growth and dissolution cycles. These investigations demonstrate the potential of {BCDI} for studying the mechanisms underlying the response of crystalline materials to external stimuli.},
	pages = {780--784},
	number = {8},
	journaltitle = {Nature Materials},
	author = {Clark, Jesse N. and Ihli, Johannes and Schenk, Anna S. and Kim, Yi Yeoun and Kulak, Alexander N. and Campbell, James M. and Nisbet, Gareth and Meldrum, Fiona C. and Robinson, Ian K.},
	date = {2015-08-28},
	eprinttype = {arxiv},
	eprint = {1501.02853},
	note = {Publisher: Nature Publishing Group},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\HYHWAC58\\nmat4320.pdf:application/pdf},
}

@article{xiong_coherent_2014,
	title = {Coherent X-ray diffraction imaging and characterization of strain in silicon-on-insulator nanostructures},
	volume = {26},
	issn = {15214095},
	doi = {10.1002/adma.201304511},
	abstract = {Coherent X-ray diffraction imaging ({CDI}) has emerged in the last decade as a promising high resolution lens-less imaging approach for the characterization of various samples. It has made signifi cant technical progress through developments in source, algorithm and imaging methodologies thus enabling important scientifi c breakthroughs in a broad range of disciplines. In this report, we will introduce the principles of forward scattering {CDI} and Bragg geometry {CDI} ({BCDI}), with an emphasis on the latter. {BCDI} exploits the ultrahigh sensitivity of the diffraction pattern to the distortions of crystalline lattice. Its ability of imaging strain on the nanometer scale in three dimensions is highly novel. We will present the latest progress on the application of {BCDI} in investigating the strain relaxation behavior in nanoscale patterned strained silicon-on-insulator ({sSOI}) materials, aiming to understand and engineer strain for the design and implementation of new generation semiconductor devices.},
	pages = {7747--7763},
	number = {46},
	journaltitle = {Advanced Materials},
	author = {Xiong, Gang and Moutanabbir, Oussama and Reiche, Manfred and Harder, Ross and Robinson, Ian},
	date = {2014-12-10},
	note = {Publisher: Wiley-{VCH} Verlag},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\JEX24KM4\\Advanced Materials - 2014 - Xiong.pdf:application/pdf},
}

@article{hua_structural_2019,
	title = {Structural insights into the formation and voltage degradation of lithium- and manganese-rich layered oxides},
	volume = {10},
	issn = {20411723},
	doi = {10.1038/s41467-019-13240-z},
	abstract = {One major challenge in the field of lithium-ion batteries is to understand the degradation mechanism of high-energy lithium- and manganese-rich layered cathode materials. Although they can deliver 30 \% excess capacity compared with today’s commercially- used cathodes, the so-called voltage decay has been restricting their practical application. In order to unravel the nature of this phenomenon, we have investigated systematically the structural and compositional dependence of manganese-rich lithium insertion compounds on the lithium content provided during synthesis. Structural, electronic and electrochemical characterizations of {LixNi}0.2Mn0.6Oy with a wide range of lithium contents (0.00 ≤ x ≤ 1.52, 1.07 ≤ y {\textless} 2.4) and an analysis of the complexity in the synthesis pathways of monoclinic-layered Li[Li0.2Ni0.2Mn0.6]O2 oxide provide insight into the underlying processes that cause voltage fading in these cathode materials, i.e. transformation of the lithium-rich layered phase to a lithium-poor spinel phase via an intermediate lithium-containing rock-salt phase with release of lithium/oxygen.},
	number = {1},
	journaltitle = {Nature Communications},
	author = {Hua, Weibo and Wang, Suning and Knapp, Michael and Leake, Steven J. and Senyshyn, Anatoliy and Richter, Carsten and Yavuz, Murat and Binder, Joachim R. and Grey, Clare P. and Ehrenberg, Helmut and Indris, Sylvio and Schwarz, Björn},
	date = {2019-12-01},
	pmid = {31772159},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\ZZG7XW5M\\s41467-019-13240-z.pdf:application/pdf},
}

@article{monga_algorithm_2021,
	title = {Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing},
	volume = {38},
	issn = {15580792},
	doi = {10.1109/MSP.2020.3016905},
	abstract = {Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.},
	pages = {18--44},
	number = {2},
	journaltitle = {{IEEE} Signal Processing Magazine},
	author = {Monga, Vishal and Li, Yuelong and Eldar, Yonina C.},
	date = {2021-03-01},
	eprinttype = {arxiv},
	eprint = {1912.10557},
	note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\2YB9YNRH\\Algorithm_Unrolling_Interpretable_Efficient_Deep_Learning_for_Signal_and_Image_Processing.pdf:application/pdf},
}

@article{favre-nicolin_free_2020,
	title = {Free log-likelihood as an unbiased metric for coherent diffraction imaging},
	volume = {10},
	issn = {20452322},
	doi = {10.1038/s41598-020-57561-2},
	abstract = {Coherent Diffraction Imaging ({CDI}), a technique where an object is reconstructed from a single (2D or 3D) diffraction pattern, recovers the lost diffraction phases without a priori knowledge of the extent (support) of the object. The uncertainty of the object support can lead to over-fitting and prevents an unambiguous metric evaluation of solutions. We propose to use a ‘free’ log-likelihood indicator, where a small percentage of points are masked from the reconstruction algorithms, as an unbiased metric to evaluate the validity of computed solutions, independent of the sample studied. We also show how a set of solutions can be analysed through an eigen-decomposition to yield a better estimate of the real object. Example analysis on experimental data is presented both for a test pattern dataset, and the diffraction pattern from a live cyanobacteria cell. The method allows the validation of reconstructions on a wide range of materials (hard condensed or biological), and should be particularly relevant for 4th generation synchrotrons and X-ray free electron lasers, where large, high-throughput datasets require a method for unsupervised data evaluation.},
	number = {1},
	journaltitle = {Scientific Reports},
	author = {Favre-Nicolin, Vincent and Leake, Steven and Chushkin, Yuriy},
	date = {2020-12-01},
	pmid = {32060293},
	eprinttype = {arxiv},
	eprint = {1904.07056},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\NGWWV998\\s41598-020-57561-2.pdf:application/pdf},
}

@article{sidky_convex_2012,
	title = {Convex optimization problem prototyping for image reconstruction in computed tomography with the {ChambollePock} algorithm},
	volume = {57},
	issn = {00319155},
	doi = {10.1088/0031-9155/57/10/3065},
	abstract = {The primaldual optimization algorithm developed in Chambolle and Pock ({CP}) (2011 J. Math. Imag. Vis. 40 126) is applied to various convex optimization problems of interest in computed tomography ({CT}) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for {CT}. The primaldual algorithm is briefly summarized in this paper, and its potential for prototyping is demonstrated by explicitly deriving {CP} algorithm instances for many optimization problems relevant to {CT}. An example application modeling breast {CT} with low-intensity x-ray illumination is presented. © 2012 Institute of Physics and Engineering in Medicine.},
	pages = {3065--3091},
	number = {10},
	journaltitle = {Physics in Medicine and Biology},
	author = {Sidky, Emil Y. and Jorgensen, Jakob H. and Pan, Xiaochuan},
	date = {2012-05-21},
	pmid = {22538474},
	eprinttype = {arxiv},
	eprint = {1111.5632},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\F362LMA3\\Sidky_2012_Phys._Med._Biol._57_3065.pdf:application/pdf},
}

@article{monga_algorithm_2021-1,
	title = {Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing},
	volume = {38},
	issn = {15580792},
	doi = {10.1109/MSP.2020.3016905},
	abstract = {Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.},
	pages = {18--44},
	number = {2},
	journaltitle = {{IEEE} Signal Processing Magazine},
	author = {Monga, Vishal and Li, Yuelong and Eldar, Yonina C.},
	date = {2021-03-01},
	eprinttype = {arxiv},
	eprint = {1912.10557},
	note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\VTS8VNQ8\\Algorithm_Unrolling_Interpretable_Efficient_Deep_Learning_for_Signal_and_Image_Processing.pdf:application/pdf},
}

@article{adler_learned_2017,
	title = {Learned Primal-dual Reconstruction},
	url = {http://arxiv.org/abs/1707.06474},
	doi = {10.1109/TMI.2018.2799231},
	abstract = {We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it does not depend on any initial reconstruction such as {FBP}. We compare performance of the proposed method on low dose {CT} reconstruction against {FBP}, {TV}, and deep learning based post-processing of {FBP}. For the Shepp-Logan phantom we obtain {\textgreater}6dB {PSNR} improvement against all compared methods. For human phantoms the corresponding improvement is 6.6dB over {TV} and 2.2dB over learned post-processing along with a substantial improvement in the {SSIM}. Finally, our algorithm involves only ten forward-back-projection computations, making the method feasible for time critical clinical applications.},
	author = {Adler, Jonas and Öktem, Ozan},
	date = {2017-07-20},
	eprinttype = {arxiv},
	eprint = {1707.06474},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\75PGLJPX\\1707.06474.pdf:application/pdf},
}

@article{atlan_imaging_2023,
	title = {Imaging the strain evolution of a platinum nanoparticle under electrochemical control},
	volume = {22},
	issn = {14764660},
	doi = {10.1038/s41563-023-01528-x},
	abstract = {Surface strain is widely employed in gas phase catalysis and electrocatalysis to control the binding energies of adsorbates on active sites. However, in situ or operando strain measurements are experimentally challenging, especially on nanomaterials. Here we exploit coherent diffraction at the new fourth-generation Extremely Brilliant Source of the European Synchrotron Radiation Facility to map and quantify strain within individual Pt catalyst nanoparticles under electrochemical control. Three-dimensional nanoresolution strain microscopy, together with density functional theory and atomistic simulations, show evidence of heterogeneous and potential-dependent strain distribution between highly coordinated (\{100\} and \{111\} facets) and undercoordinated atoms (edges and corners), as well as evidence of strain propagation from the surface to the bulk of the nanoparticle. These dynamic structural relationships directly inform the design of strain-engineered nanocatalysts for energy storage and conversion applications.},
	pages = {754--761},
	number = {6},
	journaltitle = {Nature Materials},
	author = {Atlan, Clément and Chatelier, Corentin and Martens, Isaac and Dupraz, Maxime and Viola, Arnaud and Li, Ni and Gao, Lu and Leake, Steven J. and Schülli, Tobias U. and Eymery, Joël and Maillard, Frédéric and Richard, Marie Ingrid},
	date = {2023-06-01},
	pmid = {37095227},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\VIXUU29Q\\s41563-023-01528-x.pdf:application/pdf},
}

@article{mom_deep_2023,
	title = {Deep Gauss-Newton for phase retrieval},
	volume = {2023},
	url = {https://hal.science/hal-04177989},
	doi = {10.1364/OL.484862ï},
	number = {5},
	journaltitle = {Optics Letters},
	author = {Mom, Kannara and Langer, Max and Sixou, Bruno},
	date = {2023},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\DWYPRH5U\\Optics_Letters___DGNarticle.pdf:application/pdf},
}

@report{parikh_proximal_2013,
	title = {Proximal Algorithms},
	pages = {123--231},
	author = {Parikh, N and Boyd, S and Parikh, Neal and Boyd, Stephen},
	date = {2013},
	note = {Publication Title: Foundations and Trends R in Optimization
Volume: 1
Issue: 3},
	keywords = {()},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9TD8T6PU\\prox_algs.pdf:application/pdf},
}

@article{leake_nanodiffraction_2019,
	title = {The nanodiffraction beamline {ID}01/{ESRF}: A microscope for imaging strain and structure},
	volume = {26},
	issn = {16005775},
	doi = {10.1107/S160057751900078X},
	abstract = {The {ID}01 beamline has been built to combine Bragg diffraction with imaging techniques to produce a strain and mosaicity microscope for materials in their native or operando state. A scanning probe with nano-focused beams, objectivelens-based full-field microscopy and coherent diffraction imaging provide a suite of tools which deliver micrometre to few nanometre spatial resolution combined with 10 -5 strain and 10 -3 tilt sensitivity. A detailed description of the beamline from source to sample is provided and serves as a reference for the user community. The anticipated impact of the impending upgrade to the {ESRF} – Extremely Brilliant Source is also discussed.},
	pages = {571--584},
	number = {2},
	journaltitle = {Journal of Synchrotron Radiation},
	author = {Leake, Steven J. and Chahine, Gilbert A. and Djazouli, Hamid and Zhou, Tao and Richter, Carsten and Hilhorst, Jan and Petit, Lucien and Richard, Marie Ingrid and Morawe, Christian and Barrett, Raymond and Zhang, Lin and Homs-Regojo, Roberto A. and Favre-Nicolin, Vincent and Boesecke, Peter and Schülli, Tobias U.},
	date = {2019-03-01},
	pmid = {30855270},
	note = {Publisher: Wiley-Blackwell},
	keywords = {Coherent diffraction imaging, Bragg diffraction, Full-field microscopy, Scanning microscopy, X-ray},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\7A3A57Z3\\il5024.pdf:application/pdf},
}

@article{starovoitov_comparative_2020,
	title = {Comparative analysis of the ssim index and the pearson coefficient as a criterion for image similarity},
	volume = {8},
	issn = {23089822},
	doi = {10.32523/2306-6172-2020-8-1-76-90},
	abstract = {In this paper, the {SSIM} index, which is the most popular measure of the structural image is studied. A mathematical proof that the {SSIM} index and its linear transformations are not metric functions is given. We demonstrated that this index, as well as any full-reference image comparison function, cannot evaluate the image quality. These functions estimate only some similarity degree between a reference image and its distorted copy. It is proved experimentally that the {SSIM} index does not always correctly determine similarity of images of the same scene. The Pearson linear correlation is a better tool for similarity analysis and it is faster to calculate. It is experimentally demonstrated that the Pearson correlation better than the {SSIM} index coincides with the subjective {MOS} image estimates. It is shown that the Pearson correlation coefficient is non-linearly related to the Euclid metric, but no any linear transformation of the coefficient can be a metric function. Our study proves that the Pearson correlation coefficient is superior to the {SSIM} index when evaluating image similarity.},
	pages = {76--90},
	number = {1},
	journaltitle = {Eurasian Journal of Mathematical and Computer Applications},
	author = {Starovoitov, V. V. and Eldarova, E. E. and Iskakov, K. T.},
	date = {2020},
	note = {Publisher: L.N. Gumilyov Eurasian National University},
	keywords = {Image quality, Image similarity, Metric, {MOS}, Pearson correlation, {SSIM} index},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\SGT66BGY\\ComparativeanalysisoftheSSIMindexandthepearsoncoefficientasacriterionforimagesimilarity-Star20.pdf:application/pdf},
}

@report{author_image_nodate,
	title = {Image Fusion},
	author = {{Author}},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\82T9EVRV\\image_analysis.pdf:application/pdf},
}

@article{elton_exclusion_2020,
	title = {Exclusion zone phenomena in water—a critical review of experimental findings and theories},
	volume = {21},
	issn = {14220067},
	doi = {10.3390/ijms21145041},
	abstract = {The existence of the exclusion zone ({EZ}), a layer of water in which plastic microspheres are repelled from hydrophilic surfaces, has now been independently demonstrated by several groups. A better understanding of the mechanisms which generate {EZs} would help with understanding the possible importance of {EZs} in biology and in engineering applications such as filtration and microfluidics. Here we review the experimental evidence for {EZ} phenomena in water and the major theories that have been proposed. We review experimental results from birefringence, neutron radiography, nuclear magnetic resonance, and other studies. Pollack theorizes that water in the {EZ} exists has a different structure than bulk water, and that this accounts for the {EZ}. We present several alternative explanations for {EZs} and argue that Schurr’s theory based on diffusiophoresis presents a compelling alternative explanation for the core {EZ} phenomenon. Among other things, Schurr’s theory makes predictions about the growth of the {EZ} with time which have been confirmed by Florea et al. and others. We also touch on several possible confounding factors that make experimentation on {EZs} difficult, such as charged surface groups, dissolved solutes, and adsorbed nanobubbles.},
	pages = {1--13},
	number = {14},
	journaltitle = {International Journal of Molecular Sciences},
	author = {Elton, Daniel C. and Spencer, Peter D. and Riches, James D. and Williams, Elizabeth D.},
	date = {2020-07-02},
	pmid = {32708867},
	eprinttype = {arxiv},
	eprint = {1909.06822},
	note = {Publisher: {MDPI} {AG}},
	keywords = {Diffusiophoresis, Exclusion zone, Repulsive van der Waals, Water},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\UMVGZ9XM\\EZ_water.pdf:application/pdf},
}

@article{dewan_structure_2014,
	title = {Structure of water at charged interfaces: A molecular dynamics study},
	volume = {30},
	issn = {15205827},
	doi = {10.1021/la5011055},
	abstract = {The properties of water molecules located close to an interface deviate significantly from those observed in the homogeneous bulk liquid. The length scale over which this structural perturbation persists (the so-called interfacial depth) is the object of extensive investigations. The situation is particularly complicated in the presence of surface charges that can induce long-range orientational ordering of water molecules, which in turn dictate diverse processes, such as mineral dissolution, heterogeneous catalysis, and membrane chemistry. To characterize the fundamental properties of interfacial water, we performed molecular dynamics ({MD}) simulations on alkali chloride solutions in the presence of two types of idealized charged surfaces: one with the charge density localized at discrete sites and the other with a homogeneously distributed charge density. We find that, in addition to a diffuse region where water orientation shows no layering, the interface region consists of a "compact layer" of solvent next to the surface that is not described in classical electric double layer theories. The depth of the diffuse solvent layer is sensitive to the type of charge distributions on the surface and the ionic strength. Simulations of the aqueous interface of a realistic model of negatively charged amorphous silica show that the water orientation and the distribution of ions strongly depend on the identity of the cations (Na+ vs Cs+) and are not well represented by a simplistic homogeneous charge distribution model. While the compact layer shows different solvent net orientation and depth for Na+ vs Cs+, the depth (∼1 nm) of the diffuse layer of oriented waters is independent of the identity of the cation screening the charge. The details of interfacial water orientation revealed here go beyond the traditionally used double and triple layer models and provide a microscopic picture of the aqueous/mineral interface that complements recent surface specific experimental studies. © 2014 American Chemical Society.},
	pages = {8056--8065},
	number = {27},
	journaltitle = {Langmuir},
	author = {Dewan, Shalaka and Carnevale, Vincenzo and Bankura, Arindam and Eftekhari-Bafrooei, Ali and Fiorin, Giacomo and Klein, Michael L. and Borguet, Eric},
	date = {2014-07-15},
	note = {Publisher: American Chemical Society},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\CGVCZFME\\dewan2014.pdf:application/pdf},
}

@article{feng_recent_2023,
	title = {Recent advances in water-induced electricity generation based on 2D materials: A review},
	volume = {38},
	issn = {20445326},
	doi = {10.1557/s43578-022-00811-y},
	abstract = {Two-dimensional (2D) materials with unique physical and chemical properties have attracted tremendous attentions for energy conversion applications (e.g., solar cells) in recent years. However, the periodic solar radiation somehow limits the widespread use of solar energy. Analogous to solar irradiation, water can also be found on Earth as a natural resource. Wherein, the presence of water is less restricted by the environmental conditions since it could exist in diverse forms. Therefore, harvesting green energy from water is a promising alternative to traditional energy conversion technologies. In this review, we have summarized the recent progress of water-induced electricity generation based on 2D materials. Starting with descriptions of the mechanisms proposed so far, four categories of water-induced electricity are discussed, which are across the droplet, flow, evaporation, and moisture. This review concludes with a perspective on current challenges and future directions for electricity generation from water. Graphical abstract: [Figure not available: see fulltext.].},
	pages = {1757--1779},
	number = {7},
	journaltitle = {Journal of Materials Research},
	author = {Feng, Ziheng and Zhu, Renbo and Chen, Fandi and Zhu, Yanzhe and Zhou, Yingze and Guan, Peiyuan and Kuo, Yu Chieh and Fan, Jiajun and Wan, Tao and Li, Mengyao and Han, Zhaojun and Su, Dawei and Chu, Dewei},
	date = {2023-04-14},
	note = {Publisher: Springer Nature},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\YTX8VX6M\\s43578-022-00811-y.pdf:application/pdf},
}

@article{perakis_towards_2020,
	title = {Towards molecular movies with X-ray photon correlation spectroscopy},
	volume = {22},
	issn = {14639076},
	doi = {10.1039/d0cp03551c},
	abstract = {In this perspective article we highlight research opportunities and challenges in probing structural dynamics of molecular systems using X-ray Photon Correlation Spectroscopy ({XPCS}). The development of new X-ray sources, such as 4th generation storage rings and X-ray free-electron lasers ({XFELs}), provides promising new insights into molecular motion. Employing {XPCS} at these sources allows to capture a very broad range of timescales and lengthscales, spanning from femtoseconds to minutes and atomic scales to the mesoscale. Here, we discuss the scientific questions that can be addressed with these novel tools for two prominent examples: the dynamics of proteins in biomolecular condensates and the dynamics of supercooled water. Finally, we provide practical tips for designing and estimating feasibility of {XPCS} experiments as well as on detecting and mitigating radiation damage.},
	pages = {19443--19453},
	number = {35},
	journaltitle = {Physical Chemistry Chemical Physics},
	author = {Perakis, Fivos and Gutt, Christian},
	date = {2020-09-21},
	pmid = {32870200},
	note = {Publisher: Royal Society of Chemistry},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\RYI44J7U\\d0cp03551c.pdf:application/pdf},
}

@article{vaswani_attention_2017,
	title = {Attention Is All You Need},
	url = {http://arxiv.org/abs/1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 {BLEU} on the {WMT} 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 {BLEU}. On the {WMT} 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art {BLEU} score of 41.8 after training for 3.5 days on eight {GPUs}, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	date = {2017-06-12},
	eprinttype = {arxiv},
	eprint = {1706.03762},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\8I7WGQ8T\\1706.03762.pdf:application/pdf},
}

@article{yu_ultrafast_2024,
	title = {Ultrafast Bragg coherent diffraction imaging of epitaxial thin films using deep complex-valued neural networks},
	volume = {10},
	issn = {20573960},
	doi = {10.1038/s41524-024-01208-7},
	abstract = {Domain wall structures form spontaneously due to epitaxial misfit during thin film growth. Imaging the dynamics of domains and domain walls at ultrafast timescales can provide fundamental clues to features that impact electrical transport in electronic devices. Recently, deep learning based methods showed promising phase retrieval ({PR}) performance, allowing intensity-only measurements to be transformed into snapshot real space images. While the Fourier imaging model involves complex-valued quantities, most existing deep learning based methods solve the {PR} problem with real-valued based models, where the connection between amplitude and phase is ignored. To this end, we involve complex numbers operation in the neural network to preserve the amplitude and phase connection. Therefore, we employ the complex-valued neural network for solving the {PR} problem and evaluate it on Bragg coherent diffraction data streams collected from an epitaxial La2-{xSrxCuO}4 ({LSCO}) thin film using an X-ray Free Electron Laser ({XFEL}). Our proposed complex-valued neural network based approach outperforms the traditional real-valued neural network methods in both supervised and unsupervised learning manner. Phase domains are also observed from the {LSCO} thin film at an ultrafast timescale using the complex-valued neural network.},
	number = {1},
	journaltitle = {npj Computational Materials},
	author = {Yu, Xi and Wu, Longlong and Lin, Yuewei and Diao, Jiecheng and Liu, Jialun and Hallmann, Jörg and Boesenberg, Ulrike and Lu, Wei and Möller, Johannes and Scholz, Markus and Zozulya, Alexey and Madsen, Anders and Assefa, Tadesse and Bozin, Emil S. and Cao, Yue and You, Hoydoo and Sheyfer, Dina and Rosenkranz, Stephan and Marks, Samuel D. and Evans, Paul G. and Keen, David A. and He, Xi and Božović, Ivan and Dean, Mark P.M. and Yoo, Shinjae and Robinson, Ian K.},
	date = {2024-12-01},
	note = {Publisher: Nature Research},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\VFUFCE8W\\s41524-024-01208-7.pdf:application/pdf},
}

@report{fannjiang_numerics_nodate,
	title = {{THE} {NUMERICS} {OF} {PHASE} {RETRIEVAL}},
	abstract = {Phase retrieval, i.e., the problem of recovering a function from the squared magnitude of its Fourier transform, arises in many applications such as X-ray crystallography , diffraction imaging, optics, quantum mechanics, and astronomy. This problem has confounded engineers, physicists, and mathematicians for many decades. Recently, phase retrieval has seen a resurgence in research activity, ignited by new imaging modalities and novel mathematical concepts. As our scientific experiments produce larger and larger datasets and we aim for faster and faster throughput, it becomes increasingly important to study the involved numerical algorithms in a systematic and principled manner. Indeed, the last decade has witnessed a surge in the systematic study of computational algorithms for phase retrieval. In this paper we will review these recent advances from a numerical viewpoint.},
	author = {Fannjiang, Albert and Strohmer, Thomas},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\M2HGXJM5\\ActaPhase.pdf:application/pdf},
}

@article{bendory_toward_2020,
	title = {Toward a mathematical theory of the crystallographic phase retrieval problem},
	url = {http://arxiv.org/abs/2002.10081},
	abstract = {Motivated by the X-ray crystallography technology to determine the atomic structure of biological molecules, we study the crystallographic phase retrieval problem, arguably the leading and hardest phase retrieval setup. This problem entails recovering a K-sparse signal of length N from its Fourier magnitude or, equivalently, from its periodic auto-correlation. Specifically, this work focuses on the fundamental question of uniqueness: what is the maximal sparsity level K/N that allows unique mapping between a signal and its Fourier magnitude, up to intrinsic symmetries. We design a systemic computational technique to affirm uniqueness for any specific pair (K,N), and establish the following conjecture: the Fourier magnitude determines a generic signal uniquely, up to intrinsic symmetries, as long as K{\textless}=N/2. Based on group-theoretic considerations and an additional computational technique, we formulate a second conjecture: if K{\textless}N/2, then for any signal the set of solutions to the crystallographic phase retrieval problem has measure zero in the set of all signals with a given Fourier magnitude. Together, these conjectures constitute the first attempt to establish a mathematical theory for the crystallographic phase retrieval problem.},
	author = {Bendory, Tamir and Edidin, Dan},
	date = {2020-02-24},
	eprinttype = {arxiv},
	eprint = {2002.10081},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\J4CKF8EH\\2002.10081.pdf:application/pdf},
}

@article{liu_healing_2017,
	title = {Healing X-ray scattering images},
	volume = {4},
	issn = {20522525},
	doi = {10.1107/S2052252517006212},
	abstract = {X-ray scattering images contain numerous gaps and defects arising from detector limitations and experimental configuration. We present a method to heal X-ray scattering images, filling gaps in the data and removing defects in a physically meaningful manner. Unlike generic inpainting methods, this method is closely tuned to the expected structure of reciprocal-space data. In particular, we exploit statistical tests and symmetry analysis to identify the structure of an image; we then copy, average and interpolate measured data into gaps in a way that respects the identified structure and symmetry. Importantly, the underlying analysis methods provide useful characterization of structures present in the image, including the identification of diffuse versus sharp features, anisotropy and symmetry. The presented method leverages known characteristics of reciprocal space, enabling physically reasonable reconstruction even with large image gaps. The method will correspondingly fail for images that violate these underlying assumptions. The method assumes point symmetry and is thus applicable to small-angle X-ray scattering ({SAXS}) data, but only to a subset of wide-angle data. Our method succeeds in filling gaps and healing defects in experimental images, including extending data beyond the original detector borders.},
	pages = {455--465},
	journaltitle = {{IUCrJ}},
	author = {Liu, Jiliang and Lhermitte, Julien and Tian, Ye and Zhang, Zheng and Yu, Dantong and Yager, Kevin G.},
	date = {2017},
	note = {Publisher: International Union of Crystallography},
	keywords = {data completion, image healing, inpainting, {SAXS}, X-ray scattering},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\9RFVENVB\\tj5010.pdf:application/pdf},
}

@article{sohl-dickstein_deep_2015,
	title = {Deep Unsupervised Learning using Nonequilibrium Thermodynamics},
	url = {http://arxiv.org/abs/1503.03585},
	abstract = {A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.},
	author = {Sohl-Dickstein, Jascha and Weiss, Eric A. and Maheswaranathan, Niru and Ganguli, Surya},
	date = {2015-03-12},
	eprinttype = {arxiv},
	eprint = {1503.03585},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\TAHQ3H57\\1503.03585.pdf:application/pdf},
}

@article{wang_emerging_2023,
	title = {Emerging self-sustained electricity generation enabled by moisture},
	volume = {4},
	issn = {26663864},
	doi = {10.1016/j.xcrp.2023.101517},
	abstract = {Moisture-involved electricity generation ({MIEG}) can harvest electricity from the natural hydrological process by spontaneous moisture sorption or water evaporation and thus exhibits high adaptability in various environmental and regional conditions. However, owing to the absence of sustainable and bulk water-free energy-harvesting mechanisms and structures, {MIEG} devices are limited by discontinuous and unscalable electricity generation for practical application. Through the combination of spontaneous moisture sorption and evaporation, the emergence of self-sustained moisture-involved electricity generation ({SMIEG}) presents the potential to address these challenges. In this perspective, we first introduce the operating modes and underlying mechanisms of {SMIEG}. Subsequently, we systematically summarize the progress of {SMIEG}, and emphasize some potential strategies for constructing high-performance {SMIEG} in terms of hygroscopicity, surface property, and geometry structure. Furthermore, we discuss the challenges and opportunities and envision the pathways for next-generation {SMIEG} systems through synergistic reinforcement of mechanisms, materials, and devices.},
	number = {8},
	journaltitle = {Cell Reports Physical Science},
	author = {Wang, Pengfei and Xu, Jiaxing and Wang, Ruzhu and Li, Tingxian},
	date = {2023-08-16},
	note = {Publisher: Cell Press},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\RSVE6F8U\\1-s2.0-S2666386423003016-main.pdf:application/pdf},
}

@report{schollwock_density-matrix_2004,
	title = {The density-matrix renormalization group *},
	abstract = {The density-matrix renormalization group ({DMRG}) is a numerical algorithm for the efficient trun-cation of the Hilbert space of low-dimensional strongly correlated quantum systems based on a rather general decimation prescription. This algorithm has achieved unprecedented precision in the description of one-dimensional quantum systems. It has therefore quickly acquired the status of method of choice for numerical studies of one-dimensional quantum systems. Its applications to the calculation of static, dynamic and thermodynamic quantities in such systems are reviewed. The potential of {DMRG} applications in the fields of two-dimensional quantum systems, quantum chemistry, three-dimensional small grains, nuclear physics, equilibrium and non-equilibrium statistical physics, and time-dependent phenomena is discussed. This review also considers the theoretical foundations of the method, examining its relationship to matrix-product states and the quantum information content of the density matrices generated by {DMRG}.},
	author = {Schollwöck, U},
	date = {2004},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\8KCHWBAE\\0409292v1.pdf:application/pdf},
}

@article{novikov_tensorizing_2015,
	title = {Tensorizing Neural Networks},
	url = {http://arxiv.org/abs/1509.06569},
	abstract = {Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep {VGG} networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.},
	author = {Novikov, Alexander and Podoprikhin, Dmitry and Osokin, Anton and Vetrov, Dmitry},
	date = {2015-09-22},
	eprinttype = {arxiv},
	eprint = {1509.06569},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\XHB4V4LT\\1509.06569v2.pdf:application/pdf},
}

@article{catarina_density-matrix_2023,
	title = {Density-matrix renormalization group: a pedagogical introduction},
	volume = {96},
	issn = {14346036},
	doi = {10.1140/epjb/s10051-023-00575-2},
	abstract = {Abstract: The physical properties of a quantum many-body system can, in principle, be determined by diagonalizing the respective Hamiltonian, but the dimensions of its matrix representation scale exponentially with the number of degrees of freedom. Hence, only small systems that are described through simple models can be tackled via exact diagonalization. To overcome this limitation, numerical methods based on the renormalization group paradigm that restrict the quantum many-body problem to a manageable subspace of the exponentially large full Hilbert space have been put forth. A striking example is the density-matrix renormalization group ({DMRG}), which has become the reference numerical method to obtain the low-energy properties of one-dimensional quantum systems with short-range interactions. Here, we provide a pedagogical introduction to {DMRG}, presenting both its original formulation and its modern tensor-network-based version. This colloquium sets itself apart from previous contributions in two ways. First, didactic code implementations are provided to bridge the gap between conceptual and practical understanding. Second, a concise and self-contained introduction to the tensor-network methods employed in the modern version of {DMRG} is given, thus allowing the reader to effortlessly cross the deep chasm between the two formulations of {DMRG} without having to explore the broad literature on tensor networks. We expect this pedagogical review to find wide readership among students and researchers who are taking their first steps in numerical simulations via {DMRG}. Graphic abstract: [Figure not available: see fulltext.].},
	number = {8},
	journaltitle = {European Physical Journal B},
	author = {Catarina, G. and Murta, Bruno},
	date = {2023-08-01},
	eprinttype = {arxiv},
	eprint = {2304.13395},
	note = {Publisher: Springer Science and Business Media Deutschland {GmbH}},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\JF86KVVX\\s10051-023-00575-2.pdf:application/pdf},
}

@article{hur_generative_2022,
	title = {Generative modeling via tensor train sketching},
	url = {http://arxiv.org/abs/2202.11788},
	abstract = {In this paper, we introduce a sketching algorithm for constructing a tensor train representation of a probability density from its samples. Our method deviates from the standard recursive {SVD}-based procedure for constructing a tensor train. Instead, we formulate and solve a sequence of small linear systems for the individual tensor train cores. This approach can avoid the curse of dimensionality that threatens both the algorithmic and sample complexities of the recovery problem. Specifically, for Markov models under natural conditions, we prove that the tensor cores can be recovered with a sample complexity that scales logarithmically in the dimensionality. Finally, we illustrate the performance of the method with several numerical experiments.},
	author = {Hur, {YH}. and Hoskins, J. G. and Lindsey, M. and Stoudenmire, E. M. and Khoo, Y.},
	date = {2022-02-23},
	eprinttype = {arxiv},
	eprint = {2202.11788},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\29Y2DRGP\\2202.11788v6.pdf:application/pdf},
}

@article{biamonte_tensor_2017,
	title = {Tensor Networks in a Nutshell},
	url = {http://arxiv.org/abs/1708.00006},
	abstract = {Tensor network methods are taking a central role in modern quantum physics and beyond. They can provide an efficient approximation to certain classes of quantum states, and the associated graphical language makes it easy to describe and pictorially reason about quantum circuits, channels, protocols, open systems and more. Our goal is to explain tensor networks and some associated methods as quickly and as painlessly as possible. Beginning with the key definitions, the graphical tensor network language is presented through examples. We then provide an introduction to matrix product states. We conclude the tutorial with tensor contractions evaluating combinatorial counting problems. The first one counts the number of solutions for Boolean formulae, whereas the second is Penrose's tensor contraction algorithm, returning the number of \$3\$-edge-colorings of \$3\$-regular planar graphs.},
	author = {Biamonte, Jacob and Bergholm, Ville},
	date = {2017-07-31},
	eprinttype = {arxiv},
	eprint = {1708.00006},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\BQLEDKFB\\1708.00006v1.pdf:application/pdf},
}

@article{aizpurua_tensor_2023,
	title = {Tensor Networks for Explainable Machine Learning in Cybersecurity},
	url = {http://arxiv.org/abs/2401.00867},
	abstract = {In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States ({MPS}) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that {MPS} rival traditional deep learning models such as autoencoders and {GANs} in terms of performance, while providing much richer model interpretability. Our approach naturally facilitates the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information, offering a compelling narrative for classification of anomalies and fostering an unprecedented level of transparency and interpretability, something fundamental to understand the rationale behind artificial intelligence decisions.},
	author = {Aizpurua, Borja and Palmer, Samuel and Orus, Roman},
	date = {2023-12-29},
	eprinttype = {arxiv},
	eprint = {2401.00867},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\JLQIUZWQ\\2401.00867v3.pdf:application/pdf},
}

@article{vandelli_evaluating_2023,
	title = {Evaluating the Practicality of Quantum Optimization Algorithms for Prototypical Industrial Applications},
	url = {http://arxiv.org/abs/2311.11621},
	abstract = {The optimization of the power consumption of antenna networks is a problem with a potential impact in the field of telecommunications. In this work, we investigate the application of the quantum approximate optimization algorithm ({QAOA}) and the quantum adiabatic algorithm ({QAA}), to the solution of a prototypical model in this field. We use statevector emulation in a high-performance computing environment to compare the performance of these two algorithms in terms of solution quality, using selected evaluation metrics. We estimate the circuit depth scaling with the problem size while maintaining a certain level of solution quality, and we extend our analysis up to 31 qubits, which is rarely addressed in the literature. Our calculations show that as the problem size increases, the probability of measuring the exact solution decreases exponentially for both algorithms. This issue is particularly severe when we include constraints in the problem, resulting in full connectivity between the sites. Nonetheless, we observe that the cumulative probability of measuring solutions close to the optimal one remains high also for the largest instances considered in this work. Our findings keep the way open to the application of these algorithms, or variants thereof, to generate suboptimal solutions at scales relevant to industrial use-cases.},
	author = {Vandelli, Matteo and Lignarolo, Alessandra and Cavazzoni, Carlo and Dragoni, Daniele},
	date = {2023-11-20},
	eprinttype = {arxiv},
	eprint = {2311.11621},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\8T4W6TDV\\2311.11621v2.pdf:application/pdf},
}

@article{roberts_tensornetwork_2019,
	title = {{TensorNetwork}: A Library for Physics and Machine Learning},
	url = {http://arxiv.org/abs/1905.01330},
	abstract = {{TensorNetwork} is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the {API} with applications both physics and machine learning, with details appearing in companion papers.},
	author = {Roberts, Chase and Milsted, Ashley and Ganahl, Martin and Zalcman, Adam and Fontaine, Bruce and Zou, Yijian and Hidary, Jack and Vidal, Guifre and Leichenauer, Stefan},
	date = {2019-05-03},
	eprinttype = {arxiv},
	eprint = {1905.01330},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\5EBSQ4LY\\1905.01330v1.pdf:application/pdf},
}

@article{stoudenmire_supervised_2016,
	title = {Supervised Learning with Quantum-Inspired Tensor Networks},
	url = {http://arxiv.org/abs/1605.05775},
	abstract = {Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications. We demonstrate how algorithms for optimizing such networks can be adapted to supervised learning tasks by using matrix product states (tensor trains) to parameterize models for classifying images. For the {MNIST} data set we obtain less than 1\% test set classification error. We discuss how the tensor network form imparts additional structure to the learned model and suggest a possible generative interpretation.},
	author = {Stoudenmire, E. Miles and Schwab, David J.},
	date = {2016-05-18},
	eprinttype = {arxiv},
	eprint = {1605.05775},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\SNW7BMH3\\1605.05775v2.pdf:application/pdf},
}

@article{zhang_what_2024,
	title = {What is Wrong with End-to-End Learning for Phase Retrieval?},
	url = {http://arxiv.org/abs/2403.15448},
	abstract = {For nonlinear inverse problems that are prevalent in imaging science, symmetries in the forward model are common. When data-driven deep learning approaches are used to solve such problems, these intrinsic symmetries can cause substantial learning difficulties. In this paper, we explain how such difficulties arise and, more importantly, how to overcome them by preprocessing the training set before any learning, i.e., symmetry breaking. We take far-field phase retrieval ({FFPR}), which is central to many areas of scientific imaging, as an example and show that symmetric breaking can substantially improve data-driven learning. We also formulate the mathematical principle of symmetry breaking.},
	author = {Zhang, Wenjie and Wan, Yuxiang and Zhuang, Zhong and Sun, Ju},
	date = {2024-03-17},
	eprinttype = {arxiv},
	eprint = {2403.15448},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\WG7BYNK2\\2403.15448v1.pdf:application/pdf},
}

@incollection{mieg_science_2022,
	title = {Science as a Profession: And Its Responsibility},
	volume = {57},
	abstract = {Scientific responsibility has changed with the successful professionalization of science. Today, science is a privileged profession, one with a (tacit) management mandate for systematic knowledge acquisition. Within this framework, science acts with responsibility. This chapter reflects the responsibility of science in the German context. After Wold War 2, the extraordinary responsibility of scientists, which C.F. von Weizsäcker emphasized, referred to a specific phase in the institutional development of science, termed scientism (“science justifies society,” science as religion), and corresponded to an elite responsibility. Today, one responsibility of science as a profession is to safeguard and develop scientific standards. This also concerns, on the one hand, the self-organization and control of science as a profession and, on the other hand, the communication of science to society. As a professional scientist, one has two responsibilities, the commitments to good science (professional ethics plus co-responsibility for the development of science as a profession) and civic responsibility. Due to their special knowledge, the civic responsibility of the scientist differs from that of other professionals. This chapter introduces science as a profession and presents an integrative notion of responsibility, also shedding light on the social responsibility of science.},
	pages = {67--90},
	booktitle = {Studies in History and Philosophy of Science(Netherlands)},
	publisher = {Springer Science and Business Media B.V.},
	author = {Mieg, Harald A.},
	date = {2022},
	doi = {10.1007/978-3-030-91597-1_4},
	note = {{ISSN}: 22151958},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\76QQWXUY\\science as profession and its responsibilities.pdf:application/pdf},
}

@article{renn_evolution_2018,
	title = {The Evolution of Knowledge: Rethinking Science in the Anthropocene},
	volume = {12},
	doi = {10.2478/host-2018-0001},
	abstract = {The paper argues that humanity has entered a new stage of evolution: epistemic evolution. Just as cultural evolution emerged against the background of biological evolution, epistemic evolution began as an aspect of cultural evolution and now dominates the global fate of humanity. It is characterized by the increasing dependence of global society on the achievements and further extension of science and technology in order to ensure its sustainability in the age of the Anthropocene. The historical development of knowledge is reviewed from an evolutionary perspective that introduces key concepts of an historical epistemology.},
	pages = {1--22},
	number = {1},
	journaltitle = {{HoST} - Journal of History of Science and Technology},
	author = {Renn, Jürgen},
	date = {2018-09-01},
	note = {Publisher: Walter de Gruyter {GmbH}},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\P7RXXZRU\\The_Evolution_of_Knowledge_Rethinking_Science_in_t.pdf:application/pdf},
}

@article{laski_limitations_2020,
	title = {The Limitations of the Expert},
	volume = {57},
	issn = {19364725},
	doi = {10.1007/s12115-020-00498-z},
	pages = {371--377},
	number = {4},
	journaltitle = {Society},
	author = {Laski, Harold J.},
	date = {2020-08-01},
	note = {Publisher: Springer},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\5DEWVFPS\\s12115-020-00498-z.pdf:application/pdf},
}

@report{noauthor_sequence_nodate,
	title = {Sequence Modeling: Recurrent and Recursive Nets},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\6ANL8DAZ\\RecurrentNN.pdf:application/pdf},
}

@article{gupta_patch_2023,
	title = {Patch Gradient Descent: Training Neural Networks on Very Large Images},
	url = {http://arxiv.org/abs/2301.13817},
	abstract = {Traditional {CNN} models are trained and tested on relatively low resolution images ({\textless}300 px), and cannot be directly operated on large-scale images due to compute and memory constraints. We propose Patch Gradient Descent ({PatchGD}), an effective learning strategy that allows to train the existing {CNN} architectures on large-scale images in an end-to-end manner. {PatchGD} is based on the hypothesis that instead of performing gradient-based updates on an entire image at once, it should be possible to achieve a good solution by performing model updates on only small parts of the image at a time, ensuring that the majority of it is covered over the course of iterations. {PatchGD} thus extensively enjoys better memory and compute efficiency when training models on large scale images. {PatchGD} is thoroughly evaluated on two datasets - {PANDA} and {UltraMNIST} with {ResNet}50 and {MobileNetV}2 models under different memory constraints. Our evaluation clearly shows that {PatchGD} is much more stable and efficient than the standard gradient-descent method in handling large images, and especially when the compute memory is limited.},
	author = {Gupta, Deepak K. and Mago, Gowreesh and Chavan, Arnav and Prasad, Dilip K.},
	date = {2023-01-31},
	eprinttype = {arxiv},
	eprint = {2301.13817},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\JLEH8W63\\2301.13817v1.pdf:application/pdf},
}

@article{pinckaers_streaming_2022,
	title = {Streaming Convolutional Neural Networks for End-to-End Learning with Multi-Megapixel Images},
	volume = {44},
	issn = {19393539},
	doi = {10.1109/TPAMI.2020.3019563},
	abstract = {Due to memory constraints on current hardware, most convolution neural networks ({CNN}) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for {ImageNet} and 0.001MP for {CIFAR}-10). In some domains such as medical imaging, multi-megapixel images are needed to identify the presence of disease accurately. We propose a novel method to directly train convolutional neural networks using any input image size end-to-end. This method exploits the locality of most operations in modern convolutional neural networks by performing the forward and backward pass on smaller tiles of the image. In this work, we show a proof of concept using images of up to 66-megapixels (8192×8192), saving approximately 50GB of memory per image. Using two public challenge datasets, we demonstrate that {CNNs} can learn to extract relevant information from these large images and benefit from increasing resolution. We improved the area under the receiver-operating characteristic curve from 0.580 (4MP) to 0.706 (66MP) for metastasis detection in breast cancer ({CAMELYON}17). We also obtained a Spearman correlation metric approaching state-of-the-art performance on the {TUPAC}16 dataset, from 0.485 (1MP) to 0.570 (16MP). Code to reproduce a subset of the experiments is available at https://github.com/{DIAGNijmegen}/{StreamingCNN}.},
	pages = {1581--1590},
	number = {3},
	journaltitle = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
	author = {Pinckaers, Hans and Van Ginneken, Bram and Litjens, Geert},
	date = {2022-03-01},
	pmid = {32845835},
	eprinttype = {arxiv},
	eprint = {1911.04432},
	note = {Publisher: {IEEE} Computer Society},
	keywords = {Deep learning, convolutional neural networks, high-resolution images, image classification},
	file = {PDF:C\:\\Users\\matteo1996a\\Zotero\\storage\\Y4RU7CFQ\\Streaming_Convolutional_Neural_Networks_for_End-to-End_Learning_With_Multi-Megapixel_Images.pdf:application/pdf},
}