Histogram Equalisation creating pixelated images

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jlouise2022
jlouise2022 2022년 7월 5일
댓글: jlouise2022 2022년 7월 6일
Hi there,
I am using histeq to transform a set of images. However, while some of the images are transformed fine, a fair few are looking too pixelated. I will input an example below. First, here is my code:
filenames = dir(fullfile(Stimuli, '*.jpg'));
for k = 1:numel(filenames)
images = filenames(k).name;
equalHist = imread(images);
gsImg = histeq(equalHist, 255);
[~,name,~] = fileparts(filename);
gsFilename = sprintf('%s_histeq.jpg', name);
imwrite(gsImg,gsFilename);
end
This is how some of the images are looking. E.g. edges are pixelated.
I have tried adjusting the intensity availibility , e.g. 255, 127, etc, but the problem persists.
Any ideas welcomed!
Thanks
  댓글 수: 2
DGM
DGM 2022년 7월 5일
Two things:
  • your images are integer data
  • your images are lossy-compressed JPG
What happens when you take a roughly-quantized shallow gradient and stretch its range?
A = imread('shallowgrad.jpg'); % a shallow JPG gradient
A = rgb2gray(A);
imshow(A)
B = imadjust(A); % stretch the range
imshow(B)
IA is right about not using histeq(), but the problem is more fundamental. Images have limited information. They look grossly quantized because they are. Regardless of the method, extreme contrast changes will emphasize the deficiencies of the source image.
jlouise2022
jlouise2022 2022년 7월 6일
Thanks for the explanation!

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Image Analyst
Image Analyst 2022년 7월 5일
Yes, you've learned that histogram equalized images look crappy. It's not surprising. So just don't do it. There is no need to. There are ways to get it to look less posterized (by adding a half gray level of noise) but they will still look bad so don't bother.

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