Question

6. Write Matlab code to open, cameraman.tif and then produce and display an image that is a contrasted adjusted version of the original image, with output values spanning from 0 to 255. (6 points) 255 alpMa a. 0 lo Input values high

Answer 1

Image Enhance contrast can be done using histogram equalization in matlab
In histogram equalization the work is to trying to maximize the image contrast by applying a gray level transform which tries to flatten the resulting histogram.
It turns out that the gray level transform that we are seeking is simply a scaled version of the original image's cumulative histogram.
That is, the gray level transform T is given by T[i] = (G-1)c(i), where G is the number of gray levels and c(i) is the normalized cumulative histogram of the original image.
The code is below

I=imread('cameraman.tif');
numofpixels=size(I,1)*size(I,2);
figure,imshow(I);
title('Original Image');
Histo_Img=uint8(zeros(size(I,1),size(I,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(I,1)
for j=1:size(I,2)
value=I(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(I,1)
for j=1:size(I,2)
Histo_Img(i,j)=output(I(i,j)+1);
end
end
figure,imshow(Histo_Img);
title('Histogram equalization');