Histogram Equalization for RGB Images with MATLAB Implementation

In this article, we will discuss how to implement histogram equalization for RGB images and provide corresponding source code and sample images. By implementing this process using MATLAB, we can effectively enhance image contrast and brightness distribution, resulting in clearer images that are easier to analyze. Histogram equalization is a widely used image enhancement technique that redistributes pixel values to expand the brightness range and emphasize image details. The implementation involves converting the RGB image to HSV color space, applying histogram equalization specifically to the Value (V) channel, and then converting back to RGB space. This approach preserves the original color information while enhancing luminance contrast. Key MATLAB functions used in this process include rgb2hsv for color space conversion, histeq for histogram equalization, and hsv2rgb for converting back to RGB format. The algorithm works by computing the cumulative distribution function of the pixel values in the Value channel and mapping them to create a uniform distribution. This redistribution enhances the dynamic range of the image, particularly beneficial for images with poor contrast conditions. Through the provided source code and image examples, you can better understand and apply the principles and methods of histogram equalization. The code includes comprehensive comments explaining each processing step, making it suitable for both educational purposes and practical implementation in image processing applications.