Adaptive Histogram Equalization Program (AHE)

Adaptive Histogram Equalization (AHE) is a widely used method for image enhancement. It improves image contrast and brightness by redistributing pixel values based on local histogram analysis. The AHE algorithm processes image regions independently, computing histogram equalization for each local neighborhood to achieve more uniform pixel value distribution. Key implementation aspects include: - Dividing the image into contextual regions or tiles - Calculating cumulative distribution functions for each region - Applying histogram equalization mapping to local pixels - Using interpolation methods to smooth block boundaries This approach preserves critical image details while significantly enhancing visual quality. The algorithm is particularly valuable in medical image processing and computer vision applications where local contrast enhancement is essential. Implementation typically involves functions for region partitioning, histogram calculation, and mapping function application, with careful handling of border regions through bilinear interpolation.