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Matlab code: Histogram equalization without using histeq function


              It is the re-distribution of gray level values uniformly. Let’s consider a 2 dimensional image which has values ranging between 0 and 255.




MATLAB CODE:

GIm=imread('tire.tif');
numofpixels=size(GIm,1)*size(GIm,2);
figure,imshow(GIm);
title('Original Image');


HIm=uint8(zeros(size(GIm,1),size(GIm,2)));
freq=zeros(256,1);
probf=zeros(256,1);
probc=zeros(256,1);
cum=zeros(256,1);
output=zeros(256,1);
%freq counts the occurrence of each pixel value.
%The probability of each occurrence is calculated by probf.
for i=1:size(GIm,1)
    for j=1:size(GIm,2)
        value=GIm(i,j);
        freq(value+1)=freq(value+1)+1;
        probf(value+1)=freq(value+1)/numofpixels;
    end
end
sum=0;
no_bins=255;
%The cumulative distribution probability is calculated. 
for i=1:size(probf)
   sum=sum+freq(i);
   cum(i)=sum;
   probc(i)=cum(i)/numofpixels;
   output(i)=round(probc(i)*no_bins);
end
for i=1:size(GIm,1)
    for j=1:size(GIm,2)
            HIm(i,j)=output(GIm(i,j)+1);
    end
end
figure,imshow(HIm);
title('Histogram equalization');



             




%The result is shown in the form of a table
figure('Position',get(0,'screensize'));
dat=cell(256,6);
for i=1:256
dat(i,:)={i,freq(i),probf(i),cum(i),probc(i),output(i)};   
end
   columnname =   {'Bin''Histogram''Probability''Cumulative histogram','CDF','Output'};
columnformat = {'numeric''numeric''numeric''numeric''numeric','numeric'};
columneditable =  [false false false false false false];
t = uitable('Units','normalized','Position',...
            [0.1 0.1 0.4 0.9], 'Data', dat,...
            'ColumnName', columnname,...
            'ColumnFormat', columnformat,...
            'ColumnEditable', columneditable,...
            'RowName',[]); 
    subplot(2,2,2); bar(GIm);
    title('Before Histogram equalization');
    subplot(2,2,4); bar(HIm);
    title('After Histogram equalization');




                              

Here is a simple Version of Histogram Equalization MATLAB CODE:

%Read a grayscale Image or a matrix mxn
A=imread('tire.tif');
figure,imshow(A);
%Specify the bin range[0 255]
bin=255;
%Find the histogram of the image.
Val=reshape(A,[],1);
Val=double(Val);
I=hist(Val,0:bin);
%Divide the result by number of pixels
Output=I/numel(A);
%Calculate the Cumlative sum
CSum=cumsum(Output);
%Perform the transformation S=T(R) where S and R in the range [ 0 1]
HIm=CSum(A+1);
%Convert the image into uint8
HIm=uint8(HIm*bin);
figure,imshow(HIm);



                          
                                 
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