Added Image Processing examples

ImageProcessingExamples/A-Basic_examples_and_image_compression/EXAMPLE_1_image_manipulation_examples.m

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+% EXAMPLE 1 Basic image manipulation with Matlab
+% Manel Soria ESEIAAT - 2021
+
+clear
+close
+
+img=imread('dark_horse_manel_soria.tiff'); % reads image (most formats)
+imshow(img); % displays image
+figure
+imshow(img(:,:,1)); % displays image, only red channel
+title('Only red');
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+
+class(img) % data type 
+
+% Matlab can manipulate images based on different data types
+% The most usual are:
+% uint8: 8 bits (0 to 255)
+% uint16: 16 bits (0 to 65535)
+% single, double: floating point images (0 to 1)
+
+size(img) % size (TY,TX, number of channels)
+
+small=imresize(img,0.5); % half size image
+
+figure
+imshow(small); 
+title('Original');
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+
+r=small(:,:,1); % extract red channel
+g=small(:,:,2); % green
+b=small(:,:,3); % blue
+
+r=r*2; % duplicate signal of one channel / image
+
+img2=cat(3,r,g,b); % concatenate three channels to form 
+
+figure
+imshow(img2);
+title('Double red');
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+

ImageProcessingExamples/A-Basic_examples_and_image_compression/EXAMPLE_2_jpg_quality.m

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+% EXAMPLE 2 Testing jpg image quality with Matlab
+% Manel Soria ESEIAAT - 2021
+
+clear
+close all 
+
+img=uint8(zeros(200,200,3)); % black image, uint8 (0 to 255)
+% img=uint8(ones(200,200,3)*255); % white image, uint8 (0 to 255)
+
+% draw red vertical band
+img(1:200,30:40,1)=255;
+img(1:200,30:40,2:3)=0;
+
+img(1:200,60:70,2)=255; % green
+img(1:200,60:70,1)=0;
+img(1:200,60:70,3)=0;
+
+img(1:200,90:100,3)=255; % blue
+img(1:200,90:100,1:2)=0; % blue
+
+
+imwrite(img,'test.png');
+
+set(gca,'DefaultTextFontSize',24)
+imshow(imresize(img,2));
+title('Original');
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+
+
+q=80;
+imwrite(img,'test.jpg','Quality',q);
+img2=imread('test.jpg');
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+
+figure
+imshow(imresize(img,2));
+title(sprintf('JPG quality %d',q));
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+
+figure
+
+plot(img(100,:,1));
+hold on
+plot(img2(100,:,1));
+legend({'Original','Compressed'})
+title(sprintf('Horizontal cut red channel, quality=%d',q));
+
+set(findall(gcf,'-property','FontSize'),'FontSize',18)
+

ImageProcessingExamples/B_Histogram_manipulation/B1_histogram.m

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+clear
+close all
+img=imread('low_contrast.jpg');
+
+size(img) % we see it is a monochrome image stored as a RGB, to use JPG
+
+img=img(:,:,1); % treat is as a true monochrome
+imshow(img);
+
+% find histogram (our own code)
+figure
+
+tic % just to measure computing time
+n=zeros(256,1);
+for c=0:255 % for every possible gray value
+    n(c+1)=sum(img(:)==c); % count the number of pixels of that value
+end
+plot(n)
+title('Image histogram "manual" ');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+toc
+
+
+figure
+
+% find histogram (Matlab)
+tic
+imhist(img);
+title('Image histogram with imhist');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+toc
+
+

ImageProcessingExamples/B_Histogram_manipulation/B2_manipulate_histogram_posterization.m

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+% Manel Soria - Image contrast
+
+clear
+close all
+img=imread('low_contrast.jpg');
+
+size(img) % we see it is a monochrome image stored as a RGB, to use JPG
+
+img=img(:,:,1); % treat is as a true monochrome
+imshow(img);
+title('Original image');
+
+figure
+myhist(img); % we use our histogram function
+title('Original Image histogram');
+
+
+% how can we improve contrast ?
+imgd=double(img); % we will treat the image as a double (from 0 to 1)
+m=min(imgd(:)); % min 
+M=max(imgd(:)); % max 
+imgd=(imgd-m)/(M-m); % force min to be 0, max 1
+img2=uint8(255*imgd); % convert it to 8 bit again
+
+figure
+imshow(img2);
+title('Forced to 0:255');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+figure
+myhist(img2);
+title('Forced to 0:255 histogram');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+
+
+% if we examine the histogram, we see that at both ends there is very 
+% little number of pixels, maybe we can force them to be 0 or 1, 
+% increasing even more the difference between the intermediate values
+
+imgd=double(img);
+m=min(imgd(:));
+M=max(imgd(:));
+% experiment with different values instead of 0.2 and 0.3
+m2=m+0.2*(M-m); % force m2 and below to be black 
+M2=M-0.3*(M-m); % force M2 and above to be white
+imgd=(imgd-m2)/(M2-m2);
+imgd(imgd<0)=0; % force 
+imgd(imgd>1)=1; % force
+img3=uint8(255*imgd); % this is to convert it back to 8 bits
+
+figure
+imshow(img3)
+title('Discarded blacks and whites');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+figure
+myhist(img3)
+title('Discarded blacks and whites histogram');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+
+

ImageProcessingExamples/B_Histogram_manipulation/B3_adapthisteq_imadjust.m

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+% Manel Soria - Image contrast
+
+clear
+close all
+img=imread('low_contrast.jpg');
+
+size(img) % we see it is a monochrome image stored as a RGB, to use JPG
+
+img=img(:,:,1); % treat is as a true monochrome
+imshow(img);
+title('Original');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+figure
+myhist(img);
+title('Original');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+img2=adapthisteq(img); % much better algorithm .. from help:
+%  adapthisteq Contrast-limited Adaptive Histogram Equalization (CLAHE).
+%     adapthisteq enhances the contrast of images by transforming the
+%     values in the intensity image I.  Unlike HISTEQ, it operates on small
+%     data regions (tiles), rather than the entire image. Each tile's 
+%     contrast is enhanced, so that the histogram of the output region
+%     approximately matches the specified histogram. The neighboring tiles 
+%     are then combined using bilinear interpolation in order to eliminate
+%     artificially induced boundaries.  The contrast, especially
+%     in homogeneous areas, can be limited in order to avoid amplifying the
+%     noise which might be present in the image.
+figure
+imshow(img2);
+title('adapthisteq');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+figure
+
+myhist(img2);
+title('adapthisteq histogram');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+% now we increase overall constrast
+img3=imadjust(img2,[0.2 0.8],[0 1]);
+% J = imadjust(I,[LOW_IN; HIGH_IN],[LOW_OUT; HIGH_OUT]) maps the values
+%     in intensity image I to new values in J such that values between LOW_IN
+%     and HIGH_IN map to values between LOW_OUT and HIGH_OUT. Values below
+%     LOW_IN and above HIGH_IN are clipped; 
+%     (Note that this is what we did in the previous example)
+
+figure
+imshow(img3);
+title('adapthisteq+imadjust');
+set(findall(gcf,'-property','FontSize'),'FontSize',18);
+
+figure
+myhist(img3);
+title('adapthisteq+imadjust histogram');
+
+
+

ImageProcessingExamples/B_Histogram_manipulation/B4_expose_darks.m

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+% Manel Soria 2021 - Exposure darks without loosing whites in a Lansat
+% image (single band) of the Elephant Island 
+% UPC - ESEIAAT
+
+clear
+close all
+img=imread('expose_darks_monochrome.tiff');
+imshow(img);
+title('Original image');
+
+figure
+imhist(img);
+title('Original image histogram');
+
+% 1-Convert to double (from 0 to 1)
+img2=double(img);
+img2=img2/double(max(img(:)));
+
+
+% 2-What if the image was in color ? 
+% Convert from RGB to hsv
+% hsv=rgb2hsv(imgd);
+% and then, adjust contrast of the 3th channel hsv(:,:,3) 
+% finally, convert the image back to RGB
+% BUT: be aware that color images are more difficult to handle
+
+% 3-Adjust local contrast
+% Contrast-limited adaptive histogram equalization (CLAHE)
+% MORE LOCAL CONTRAST: More risk for posterization
+% WARNING: NON-UNIFORM TREATMENT, PHOTOMETRIC INFO IS LOST
+%img3=adapthisteq(img2, 'NumTiles',[16 16],'ClipLimit',0.025) ;
+
+% This is more conservative
+img3=adapthisteq(img2, 'NumTiles',[16 16],'ClipLimit',0.01) ; 
+
+figure
+imshow(img3);
+title('adapthisteq');
+
+% 4-Increase exposure dark areas
+% This is homogeneous (in all image) 
+% An easy way that rise darks is to use functions y=x.^p; with p<1
+
+imgd4=img3.^0.25;
+
+figure
+imshow(imgd4);
+title('adapthisteq + ^1/4 ');
+
+% 5-Adjust overall contrast (discarding some blacks and some whites)
+
+% First we check the histogram to select the values 
+figure
+imhist(imgd4);
+title('adapthisteq + ^1/4 Histogram');
+
+imgd5=imadjust(imgd4,[0.45 0.99],[0 1]); % this is, below 0.45 will be 0
+figure
+imshow(imgd5);
+title('adapthisteq + ^1/4 + imadjust');
+
+% show the final histogram .. be careful with posterization
+figure
+imhist(imgd5);
+title('Final histogram');
+
+% 6- Convert back to 16 bit and save
+img6=uint16((2^16-1)*imgd5);
+imwrite(img6,'out.tiff','tif');
+
+

ImageProcessingExamples/B_Histogram_manipulation/myhist.m

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+function myhist( img )
+% plots histogram of a uint8 monochrome image
+
+for c=0:255 
+    n(c+1)=sum(img(:)==c);
+end
+
+plot(n)
+end
+