Histogram Equalization

The implementation of histogram equalization code in MATLAB can be achieved with remarkably simple and clever programming techniques. Histogram equalization is an image processing technique used to enhance image contrast and brightness by redistributing pixel values throughout the image. In MATLAB, you can implement histogram equalization with just a few lines of code using built-in functions and custom algorithms. The process begins by reading the input image and converting it to grayscale format using functions like imread() and rgb2gray(). Next, calculate the image histogram using the imhist() function, which provides the frequency distribution of pixel intensity values. Based on this histogram, compute the cumulative distribution function (CDF) that determines how pixel values will be remapped. This CDF is then normalized and scaled to map the original pixel values to a new range that spans the full intensity spectrum (typically 0-255). The actual transformation is performed using the histeq() function or custom mapping algorithms that apply the calculated CDF to each pixel. Finally, display the enhanced image using imshow() and save the processed result with imwrite(). The core algorithm involves calculating the transformation function T(r) = (L-1) * cumulative_sum(histogram) / total_pixels, where L represents the number of possible intensity levels. Overall, histogram equalization represents a straightforward yet powerful image enhancement technique that can be efficiently implemented in MATLAB with both built-in functions and custom code approaches for educational and practical applications.