perform the following operations on the images you have taken. a. apply histogram equalization to the image. do not use matlab functions (histeq) for histogram equalization. write your own code in matlab

main.m
% Read the input image
inputImage = imread('your_image.jpg');

% Convert the image to grayscale
grayImage = rgb2gray(inputImage);

% Calculate the histogram of the input image
pixelCounts = zeros(256, 1);
for i = 1:size(grayImage, 1)
    for j = 1:size(grayImage, 2)
        pixelIntensity = grayImage(i, j);
        pixelCounts(pixelIntensity + 1) = pixelCounts(pixelIntensity + 1) + 1;
    end
end

% Calculate the cumulative distribution function (CDF)
histogramCumulative = cumsum(pixelCounts);

% Normalize the CDF
cdfNormalized = histogramCumulative / sum(pixelCounts);

% Map the pixels of the input image to new intensity values based on the normalized CDF
mappedImage = zeros(size(grayImage));
for i = 1:size(mappedImage, 1)
    for j = 1:size(mappedImage, 2)
        pixelIntensity = grayImage(i, j);
        mappedIntensity = round(cdfNormalized(pixelIntensity + 1) * 255);
        mappedImage(i, j) = mappedIntensity;
    end
end

% Display the original and histogram-equalized images
figure;
subplot(1, 2, 1), imshow(grayImage), title('Original Image');
subplot(1, 2, 2), imshow(uint8(mappedImage)), title('Histogram Equalized Image');
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