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Tito on 18 Nov 2021
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Commented: DGM on 17 May 2024 at 14:55
Hi guys, I need to do an histogram equalization without using the command "histeq" but I need that in color. All the helps that i've found are in gray scale, I need to get an image equalizated that looks like the one that histeq throws or at least very similar.
I hope you can help me because i've been trying to do it and I always end up with an image with a lot of intensity to the point that it looks way more worst than at the beginning.
Thank you!
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Answers (1)
DGM on 18 Nov 2021
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Edited: DGM on 18 Nov 2021
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It all depends on why you can't use histeq(). If you can't use histeq() because you don't have IPT, then MIMT has a drop-in replacement that would work:
nbins = 64;
A = imread('peppers.png');
B = zeros(size(A),class(A));
for c = 1:size(A,3)
% this uses the default 64-bin flat histogram
% you can explicitly specify otherwise
B(:,:,c) = histeq(A(:,:,c),nbins);
end
subplot(2,2,1)
imshow(A)
subplot(2,2,2)
imshow(B)
colors = {'r','g','b'};
subplot(2,2,3)
hold on
for c = 1:size(A,3)
[n,x] = imhist(A(:,:,c),64); % same as MIMT imhistFB()
stem(x,n,colors{c},'marker','none')
end
subplot(2,2,4)
hold on
for c = 1:size(A,3)
[n,x] = imhist(B(:,:,c),64);
stem(x,n,colors{c},'marker','none')
end
If you're avoiding histeq() because this is homework and you're supposed to write it from scratch, then just open histeq() and look at the code and documentation to understand how it works.
Nothing about histogram equalization should imply that the result "looks better". It usually looks awful; the goal is to enforce some desired intensity distribution on the image to facilitate further processing. Maybe you're expecting something more like CLAHE?
A = imread('peppers.png');
B = zeros(size(A),class(A));
for c = 1:size(A,3)
% same as MIMT adapthisteqFB()
B(:,:,c) = adapthisteq(A(:,:,c));
end
figure;
imshow(B)
You may also elect to do either of these tasks by converting the image to some other model (e.g. LAB) and then doing equalization on the brightness component of the converted image. That may yield more desirable results. If you don't have IPT and need conversion tools, there are alternatives.
nbins = 64;
mode = 'lab'; % pick something
A = imread('peppers.png');
switch mode
case 'hsv'
B = rgb2hsv(A); % not in IPT
%B(:,:,3) = histeq(B(:,:,3),nbins);
B(:,:,3) = adapthisteq(B(:,:,3));
B = im2uint8(hsv2rgb(B));
case 'lab'
B = rgb2lab(A); % MIMT has rgb2lch and others
%B(:,:,1) = histeq(B(:,:,1)/100,nbins)*100;
B(:,:,1) = adapthisteq(B(:,:,1)/100)*100;
B = im2uint8(lab2rgb(B));
end
figure
imshow(B)
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Tito on 18 Nov 2021
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https://matlabcentral.mathworks.com/matlabcentral/answers/1589559-histogram-equalization-without-histeq-color#comment_1840174
Yes, I'm avoiding histeq() because this is homework and I suppos to write it from scratch and then compare it with the result of run it through histeq(). I tried with the documentation but it only talks about the grayscale case or that's what I understand.
And what I mean by "it looks worse" is that I'm producing this:
While histeq() is making this:
DGM on 17 May 2024 at 14:55
Direct link to this comment
https://matlabcentral.mathworks.com/matlabcentral/answers/1589559-histogram-equalization-without-histeq-color#comment_3164321
I don't know why I never responded here, but this sort of result should immediately make you suspect that the output is improperly scaled for its class -- probably because something beforehand was as well. This often occurs if you change working class without rescaling (e.g. using double() instead of im2double()). It can also happen by preallocating the output array implicitly as 'double', and then populating it with (e.g.) uint8-scale data.
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