Histogram Equalization for Color Images

Histogram equalization applied to color images after acquisition serves as an effective image enhancement technique. This method achieves uniform brightness distribution by equalizing color components, commonly implemented through two primary approaches: processing RGB channels independently or converting to HSV/HSI color space and applying equalization solely to the intensity/value component. The algorithm typically involves calculating cumulative distribution functions for each channel and mapping pixel values to enhance contrast. Widely adopted in image processing and computer vision domains, this technique finds applications in digital image processing, medical imaging, security surveillance systems, and more. Furthermore, histogram equalization can be integrated with other enhancement methods such as sharpening filters (e.g., Laplacian or unsharp masking) and noise reduction filters (e.g., Gaussian or median filters) to further improve image quality and analytical capabilities. Key implementation considerations include color preservation when processing individual RGB channels and maintaining color fidelity during color space conversions.