Adaptive Histogram Equalization Algorithm for Enhanced Image Processing

The Adaptive Histogram Equalization algorithm serves as an effective method for processing color images, demonstrating particular efficacy in medical imaging and endoscopic image applications. Implementation-wise, this algorithm operates by dividing the image into small contextual regions (tiles) and applying histogram equalization to each tile individually, followed by bilinear interpolation to eliminate artificial boundaries. This dynamic pixel value adjustment significantly enhances image quality and detail visibility by improving local contrast and brightness distribution, resulting in clearer images that facilitate more accurate analysis. Consequently, this algorithm holds extensive application potential in medical imaging fields, providing physicians and researchers with superior image analysis tools and diagnostic support. Beyond medical applications, the Adaptive Histogram Equalization algorithm finds utility in various domains including computer vision, image processing, and pattern recognition systems. When processing color images, the algorithm typically converts RGB images to HSV/HSL color space and applies equalization only to the luminance/value channel to preserve original color information while enhancing detail and color depth. This approach delivers more comprehensive and accurate image analysis results, making the Adaptive Histogram Equalization algorithm a highly valuable image processing technique with broad application prospects and significant research value.