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I needed to plot Histograms (or) Loss Information for original image and after restoration of blur image

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Kartikeya
Kartikeya 2023-10-20
评论： DGM 2023-10-21
Ran in:
%% Input Image
Ioriginal = imread('greyface.jpg');
imshow(Ioriginal)
title('Original Image')
% % Restore Motion Blur Without Noise
PSF = fspecial('motion',21,11);
Idouble = im2double(Ioriginal);
blurred = imfilter(Idouble,PSF,'conv','circular');
imshow(blurred)
title('Blurred Image')
%%Restoring blur image
wnr1 = deconvwnr(blurred,PSF);
imshow(wnr1)
title('Restored Blurred Image')
%%Restore Motion Blur and Gaussian Noise
noise_mean = 0;
noise_var = 0.0001;
blurred_noisy = imnoise(blurred,'gaussian',noise_mean,noise_var);
imshow(blurred_noisy)
title('Blurred and Noisy Image')
wnr2 = deconvwnr(blurred_noisy,PSF);
imshow(wnr2)
title('Restoration of Blurred Noisy Image (NSR = 0)')
signal_var = var(Idouble(:));
NSR = noise_var / signal_var;
wnr3 = deconvwnr(blurred_noisy,PSF,NSR);
imshow(wnr3)
title('Restoration of Blurred Noisy Image (Estimated NSR)')

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回答（1 个）

Image Analyst
Image Analyst 2023-10-20
Why not use imabsdiff to get the difference between the original image and the restored image and then take the histogram of that difference image?
diffImage = imabsdiff(Ioriginal, wnr1);
histogram(diffImage);
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DGM
DGM 2023-10-21
Ran in:
Ioriginal = imread('cameraman.tif');
% Restore Motion Blur Without Noise
PSF = fspecial('motion',21,11);
Idouble = im2double(Ioriginal);
blurred = imfilter(Idouble,PSF,'conv','circular');
% Restoring blur image
wnr1 = deconvwnr(blurred,PSF);
% assuming the abs difference is the thing you actually want
err = imabsdiff(Idouble,wnr1);
histogram(err)
% Restore Motion Blur and Gaussian Noise
noise_mean = 0;
noise_var = 0.0001;
blurred_noisy = imnoise(blurred,'gaussian',noise_mean,noise_var);
signal_var = var(Idouble(:));
NSR = noise_var / signal_var;
wnr3 = deconvwnr(blurred_noisy,PSF,NSR);
% assuming the abs difference is the thing you actually want
err = imabsdiff(Idouble,wnr3);
histogram(err)

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