Local Histogram Equalization Algorithm

A local histogram equalization algorithm designed to enhance image contrast and detail by processing images through localized histogram equalization operations. The algorithm requires two input parameters: the source image and window dimensions (width and height), with both dimensions constrained to odd numbers to ensure each window has a central pixel for proper processing. The algorithm works by dividing the input image into multiple overlapping windows and performing histogram equalization within each local window. This approach improves the gray-level distribution in every local region of the image, resulting in enhanced visual quality and better detail preservation compared to global histogram equalization methods. Key implementation aspects include: - Window sliding mechanism with central pixel alignment - Local histogram calculation for each window region - Cumulative distribution function (CDF) computation for equalization - Boundary handling for image edges - Optional padding strategies for incomplete windows This algorithm is particularly valuable in image enhancement and digital image processing applications, offering significant improvements in medical imaging, surveillance systems, and photographic enhancement where local contrast adjustment is required. The odd-number window constraint ensures symmetric processing around each pixel while maintaining computational efficiency through optimized sliding window operations.