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How to Normalize a Histogram in MATLAB?
A histogram is nothing but a graphical representation that shows the distribution of a set of data points. The normalization of a histogram is a process of distributing its frequencies over a wide range.
Before discussing the implementation of histogram normalization in MATLAB, let us first get an overview of histogram normalization.
What is Histogram Normalization?
A histogram is a graphical way of representing the distribution of frequencies of a dataset. Sometimes, we see a histogram in which the frequencies are distributed in a small range. It results in producing poor contrast in a digital image.
There is a technique called "histogram normalization" that is used to distribute the frequencies of datasets over a wide range.
In digital image processing, histogram normalization is used to improve the contrast levels in an image.
Mathematically, the histogram normalization is performed using the following formula,
$$\mathrm{Hist. Norm=\frac{(Intensity − Min value)}{(Max \:Value − Min \:Value)}× 255}$$
Now, let us discuss the process of histogram normalization in MATLAB.
Histogram Normalization in MATLAB
In MATLAB, the normalization of a histogram is performed as per the following steps −
Step (1) − Read the digital image whose histogram is to be normalized. For this, use the "imread" function.
Step (2) − Convert the input image to gray scale if required. For this, use the "rgb2gray" function.
Step (3) − Convert the grayscale image to double data type for calculations. For this, use the "double" function.
Step (4) − Specify the minimum and maximum values required for histogram normalization.
Step (5) − Use the histogram normalization formula to perform the normalization of the histogram.
Step (6) − Display the result.
Example
Let us take an example to understand the implementation and execution of these steps to perform histogram normalization of an image.
% MATLAB code to perform histogram normalization
% Read the input image
img = imread('https://www.tutorialspoint.com/assets/questions/media/14304-1687425269.jpg');
% Convert the input image to grayscale
gray_img = rgb2gray(img);
% Convert the grayscale image to double datatype
double_img = double(gray_img);
% Specify the minimum and maximum values for histogram normalization
min_value = 50;
max_value = 170;
% Perform histogram normalization
norm_img = (double_img - min_value) / (max_value - min_value);
% Scale the normalized image
scaled_img = norm_img * 255;
% Convert the scaled image to uint8 to display
hist_norm_img = uint8(scaled_img);
% Display the input image, normalized image, and their histograms
figure;
subplot(2, 2, 1);
imshow(gray_img);
title('Input Image');
subplot(2, 2, 2);
imhist(gray_img);
title('Input Image Histogram');
subplot(2, 2, 3);
imshow(hist_norm_img);
title('Normalized Image');
subplot(2, 2, 4);
imhist(hist_norm_img);
title('Normalized Histogram');
Output
When you run this code, it will produce the following output −
Conclusion
In conclusion, the histogram normalization is a technique of distributing the frequencies of a dataset over a wide range to improve contrast levels. In this tutorial, I explained the stepbystep process of histogram normalization of an image with the help of an example in MATLAB.
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