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');

To find the histogram of an Image:  http://angeljohnsy.blogspot.com/2011/06/histogram-of-image.html


Local histogram equalization of an Image:  http://angeljohnsy.blogspot.com/2011/06/local-histogram-equalization.html

                              

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);

                          
                                 

YIQ Image to RGB Image

MATLAB code:
%YIQ to RGB

%R=Y+0.956*I+0.621*Q
%G=Y-0.272*I-0.647*Q
%B=Y-1.106*I+1.703*Q
RGB=uint8(zeros(size(YIQ)));
for i=1:size(YIQ,1)
    for j=1:size(YIQ,2)
          RGB(i,j,1)=YIQ(i,j,1)+0.956*YIQ(i,j,2)+0.621*YIQ(i,j,3);
          RGB(i,j,2)=YIQ(i,j,1)-0.272*YIQ(i,j,2)-0.647*YIQ(i,j,3);
          RGB(i,j,3)=YIQ(i,j,1)-1.106*YIQ(i,j,2)+1.703*YIQ(i,j,3);
    end
end

figure,imshow(RGB);
title('RGB Image');

RGB Image to YIQ Image

YIQ  builds the basis for the NTSC (National Television System Commitee) format.
The Component division of YIQ :
Y=0.30R+0.59G+0.11B
I=0.60R-0.28G-0.32B
Q=0.21R-0.52G+0.31B


MATLAB code:
Im=imread('peppers.png');


figure,imshow(Im);
title('Original Image')

%y=0.2989 * R + 0.5870 * G + 0.1140 * B 

%I=0.60*R - 0.28*G-0.32*B

%Q=0.21*R -0.52*G+0.31*B

YIQ=uint8(zeros(size(Im)));
for i=1:size(Im,1)
    for j=1:size(Im,2)
        YIQ(i,j,1)=0.2989*Im(i,j,1)+0.5870*Im(i,j,2)+0.1140*Im(i,j,3);
        YIQ(i,j,2)=0.596*Im(i,j,1)-0.274*Im(i,j,2)-0.322*Im(i,j,3);
        YIQ(i,j,3)=0.211*Im(i,j,1)-0.523*Im(i,j,2)+0.312*Im(i,j,3);
    end
end
figure,imshow(YIQ);
title('YIQ Image');