图像修复基于matlab损坏图像修复【含Matlab源码 731期】.zip

clc; clear all; %First part %reading img1, saving as im1 variable im1 = imread('img1.jpg'); %for boundary it's a must to convert to gray im1 = rgb2gray(im1); subplot(331), imshow(im1), title('Img1 convert to gray'); % as a mask all colors will be white or black mask = im1 < 255; subplot(332), imshow(mask), title('Apply a mask'); %invert colors, otherwise the boundary sees nearly %the whole image, need to swap colors mask = imcomplement(mask); subplot(333), imshow(mask), title('Invert colors'); %gives location x y coordinates where is the first area, %it starts there too %if don't have ; at the end, it shows the dimensions of %the image dim = size(mask) col = round(dim(2)/2)-90; row = min(find(mask(:,col))) %an interesting drawback, if I would fill the "holes" %with erode function which is better then imfill, the %program can't see any of the boundaries, hence in the %for loop I had to use 180 rounds, otherwise can't see %the big parts. %erode image % se = strel('line',10, 90); % boundaries = imerode(mask,se); % imshow(boundaries); %use bwtraceboundary function to find the boundary from %the above declared point. %it needs a binary image, row and col coordinates for start %and direction, W for west so left. boundary = bwtraceboundary(mask,[row, col],'W'); subplot(334), imshow(im1), title('Boundaries'); hold on; plot(boundary(:,2),boundary(:,2),'g','LineWidth',4); %the imfill fills the smaller objects. However, as I %mentioned above the erode function does better job. %Unfortunately, with that result the for loop cannot %find any boundaries, despite the imshow(mask)black&white %image looks better. BW_filled = imfill(mask,'holes'); boundaries = bwboundaries(BW_filled); %shows the border of all white part, with the imfill %function the for loop must iterate 180 times, hilarious. %Under 180 it doesn't find the last top right big white %object. for k=1:180 b = boundaries{k}; plot(b(:,2),b(:,1),'g','LineWidth',4); end %Second part %denoise test; averaging or median filter im2 = imread('Penguins.jpg'); im2 = imresize(im2, [768, 1024]); rgbImage = im2; subplot(335), imshow(rgbImage), title('Resized but noisy Img2'); %averaging filter mat = ones(5,5)/25; averagingFilter_im2 = imfilter(rgbImage, mat); subplot(336), imshow(averagingFilter_im2), title('Img2 averaging filter'); %median filter for k=1:3 medianFilter_im2(:,:,k)=medfilt2(rgbImage(:,:,k),[3,3]); end subplot(337), imshow(medianFilter_im2), title('Img2 median filter'); %Third part im1 = imread('img1.jpg'); recoveredImage = im1; recoveredImageMedianFilter = im1; zero = recoveredImage == 255; recoveredImage(zero) = averagingFilter_im2(zero); zero1 = recoveredImageMedianFilter == 255; recoveredImage(zero1) = averagingFilter_im2(zero1); recoveredImageMedianFilter(zero1) = medianFilter_im2(zero); subplot(338), imshow(recoveredImage), title('Recovered image with averaging filter'); subplot(339), imshow(recoveredImageMedianFilter), title('Recovered image with median filter'); %Compare recoveredImage and originalImage originalImage = imread('Penguins.jpg'); %Writes out in command window meanRecoveredIm = mean(recoveredImage(:)) meanOriginalIm = mean(originalImage(:)) %It coutns the Structural Similarity Index (SSIM) value %for original and recovered image. ssimValue = ssim(originalImage, recoveredImage) %It counts the Peak Signal to Noise Ratio value %for original and recovered image. They must be the same %class and size as well. peaks2NoiseRatio = psnr(originalImage, recoveredImage) %It counts the Mean Squared Error (MSE) between arrays %of the 2 declared variable, currently the original and the recovered image. meanSquaredErr = immse(originalImage, recoveredImage) %write out image as a file imwrite(recoveredImage, 'recoveverdImage.jpg');