datasets.md

Datasets

Name	Description	Link
GoPro	The GoPro dataset consists of 3,214 pairs of motion-blurred and sharp images, each with a resolution of 1,280×720 pixels, divided into 2,103 training pairs and 1,111 test pairs.	GoPro
REDS	The REalistic and Dynamic Scenes (REDS) dataset is generated from 120 fps videos, with blurry frames synthesized by merging consecutive frames, capturing realistic motion blur in dynamic scenes.	REDS
DPDD	The Dual-Pixel Defocus Deblurring (DPDD) dataset contains 500 carefully captured scenes, comprising 2000 images in total: 500 defocus-blurred images with their 1000 dual-pixel (DP) sub-aperture views and 500 corresponding all-in-focus images, all at full-frame resolution of 6720x4480 pixels.	DPDD
HIDE	The HIDE (Human-aware Image Deblurring) dataset consists of 8,422 blurred images paired with their corresponding sharp images, focusing on motion deblurring with an emphasis on human subjects, making it ideal for human-centric deblurring tasks.	HIDE
RealBlur	The RealBlur dataset consists of 4,738 pairs of images from 232 different scenes, captured in both camera raw and JPEG formats. It is divided into two subsets: RealBlur-R with raw images and RealBlur-J with JPEG images, with 3,758 training pairs and 980 test pairs in each subset.	RealBlur
CelebA	The CelebFaces Attributes dataset (CelebA) is a large-scale face attributes dataset comprising 202,599 images of 10,177 celebrities. Each image is 178×218 pixels and annotated with 40 binary labels for facial attributes like hair color, gender, and age.	CelebA
Deblur-NeRF	The Deblur-NeRF dataset focuses on two types of blur: camera motion blur and defocus blur. It includes 5 synthesized scenes for each blur type, created using Blender with multi-view cameras to simulate real data capture. For motion blur, images are rendered from interpolated camera poses, while defocus blur images are generated with depth-of-field effects. Additionally, the dataset features 20 real-world scenes—10 for each blur type—captured with a Canon EOS RP, including both manually blurred images and sharp reference images.	Deblur-NeRF
RSBlur	The RSBlur dataset offers pairs of real and synthetic blurred images, each with corresponding ground truth sharp images. It is designed to evaluate deblurring and blur synthesis methods on real-world blurred images, with training, validation, and test sets comprising 8,878, 1,120, and 3,360 blurred images, respectively.	RSBlur