IMAGE PROCESSING

" Two roads diverged in a wood, and I,
I took the one less traveled by,
And that has made all the difference "-Robert Frost

COLOR HISTOGRAM EQUALIZATION - MATLAB CODE

Histogram Equalization can be considered as redistribution of the intensity of the image. Color histogram equalization can be achieved by converting a color image into HSV/HSI image and enhancing the Intensity while preserving hue and saturation components.

However, performing histogram equalization on components of R,G and B independently will not  enhance the image. At the end of this post, check the histogram of before and after histogram equalization of an image which is obtained by performing histogram equalization on the components(R,G and B) independently.

Steps to be performed:
2.      Obtain the ‘Intensity Matrix’ from the HSI Image matrix
3.      Perform Histogram Equalization on the intensity Matrix
http://angeljohnsy.blogspot.com/2011/04/matlab-code-histogram-equalization.html
4.      Update the Intensity Matrix from the HSI Image matrix with the histogram equalized Intensity matrix

MATLAB CODE:
%COLOR HISTOGRAM EQUALIZATION

%CONVERT THE RGB IMAGE INTO HSV IMAGE FORMAT
HSV = rgb2hsv(I);

%PERFORM HISTOGRAM EQUALIZATION ON INTENSITY COMPONENT
Heq = histeq(HSV(:,:,3));

HSV_mod = HSV;
HSV_mod(:,:,3) = Heq;

RGB = hsv2rgb(HSV_mod);

figure,subplot(1,2,1),imshow(I);title('Before Histogram Equalization');

subplot(1,2,2),imshow(RGB);title('After Histogram Equalization');

EXPLANATION:

RGB image matrix is converted into HSI(Hue ,Saturation and Intensity) format and histogram equalization is applied only on the Intensity matrix . The Hue and Saturation matrix remains the same. The updated HSI image matrix is converted back to RGB image matrix.

%DISPLAY THE HISTOGRAM OF THE ORIGINAL AND THE EQUALIZED IMAGE

HIST_IN = zeros([256 3]);
HIST_OUT = zeros([256 3]);

%http://angeljohnsy.blogspot.com/2011/06/histogram-of-image.html
%HISTOGRAM OF THE RED,GREEN AND BLUE COMPONENTS

HIST_IN(:,1) = imhist(I(:,:,1),256); %RED
HIST_IN(:,2) = imhist(I(:,:,2),256); %GREEN
HIST_IN(:,3) = imhist(I(:,:,3),256); %BLUE

HIST_OUT(:,1) = imhist(RGB(:,:,1),256); %RED
HIST_OUT(:,2) = imhist(RGB(:,:,2),256); %GREEN
HIST_OUT(:,3) = imhist(RGB(:,:,3),256); %BLUE

mymap=[1 0 0; 0.2 1 0; 0 0.2 1];

figure,subplot(1,2,1),bar(HIST_IN);colormap(mymap);legend('RED CHANNEL','GREEN CHANNEL','BLUE CHANNEL');title('Before Applying Histogram Equalization');
subplot(1,2,2),bar(HIST_OUT);colormap(mymap);legend('RED CHANNEL','GREEN CHANNEL','BLUE CHANNEL');title('After Applying Histogram Equalization');

EXPLANATION:
Obtain the histogram of each component (Red,Green and Blue) independently.
Define the colormap ‘mymap’ with three colors namely Red, Green and Blue.
Display the histograms of the components before and after histogram equalization.

NOTE:
Histogram of the above image by processing the components independently gives bad result.

 Histogram Equalization applied on individual components
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Unknown said...

very good explanation of color image histogram

Unknown said...

how can I do a histogram equalization on each channel and get the same results as shown above, but without using the inbuilt matlab functions