```
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

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);
```

## 29 comments:

its nice that this space provides output too..and most importantly..the code works!!unlike most of the other sites!

-thank u soo much for this code.

good, very good

its last part is not working 3rd fig does not plot.ERROR::::bar must be 2 -D;

help me out

Thanx in advance...

A graphical representation which shows a visual impression of the distribution of data termed as Histogram . Histograms consists of tabular frequencies which are shown as adjacent rectangles, with an area equal to the frequency of the observations in the interval.

@cool_images

just change the original image to gray-scale value, because the error occur and mention that only 2D

i had change in the end of the code as follows:

GIm1=rgb2gray(GIm);

subplot(2,2,2);

bar(GIm1);

title('Before Histogram equalization');

subplot(2,2,4);

bar(HIm);

title('After Histogram equalization');

Nice.... its very helpful

can i know, what is that mean with 1 and 2 in this code?

numofpixels=size(GIm,1)*size(GIm,2);

thanks.. ^^

@sumi

it represents the dimensions. For a matrix, 1 represents rows and 2 represents columns.

This is very nice tutorial here. Can you provide me with the code for CLAHE.

Is there a bit problem with code, because max value of uint8 is 255, then

freq(value+1)=freq(value+1)+1;

cannot apply for the case value = 255. Further more, you set max gray value is 255, it is only right in almost case, not all case.

@socksau

In matlab, the array range can start from 1. So the actual range 0 to 255 is mapped as 1 to 256.

can anybody give me code of face recognition system using k-means clustering algorithm and PCA .

After changing image to grayscale also, the last output is not working.. uitable is showing as unrecognised parameter : units.

@Aaron Angel 1 means rows and 2 means columns of the image

i really doint understand how the freq funtion works someone care to explain?

whether histogram functions can be applied to rgb image or only to gray scale???

nice, could you generalize median filter to any size of the window?

I had the same problem with Rohit Ramesh (April 4, 2013). The 3rd figure shows nothing and the error as below:

Error using uitable

Bad property value found.

Object Name : uitable

Property Name : 'Units'.

Error in uitable (line 52)

thandle = builtin('uitable', varargin{:});

Error in territory7 (line 46)

t = uitable('Units','normalised','Position',...

Love this blog. :) Good stuff! Keep it up!

UPLOAD SAME KIND PROBLEMS

CAN U GIVE ME CODE FOR COMPARING TWO IMAGES IN MATLAB I WANT TO DETECT FAULT IN FABRIC

Can you give me code for IMAGE FUSION using Improved Synthetic Variable Ratio method? Please sir...

freq=zeros(256,1);

probf=zeros(256,1);

probc=zeros(256,1);

cum=zeros(256,1);

output=zeros(256,1);

what is the meaning of each line above??, please quick answer :) :)

@ahmed ali

Pre-allocation of the vectors or 1D arrays. The gray level is[0 255] so there are 256 elements in total.

how to write a function to equilize a 100-by-100 image of normal random numbers with mean 128 and std-dev of 50.

can anybody provide full coding for image enhancement using histogram equalizatio..its urgent

histogram are also found different from that obtained from imhist(histeq(HIm))

how about GIm,3 and GIm,4?

How do i get the rgb for the pic?

its amazing; help us with a similar code for bi-histogram equalization also

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