Adaptive Histogram Equalization Algorithm Implementation

This article provides a comprehensive overview of implementing the Adaptive Histogram Equalization (AHE) algorithm, an advanced image enhancement technique that improves contrast and clarity in digital images. We begin by explaining the core principles and advantages of AHE, highlighting its ability to locally adapt histogram distribution rather than applying global transformations. The implementation section details key steps including: dividing images into contextual regions using sliding windows, computing localized histograms for each sub-region, and applying histogram equalization through cumulative distribution functions. Code implementation typically involves functions for tile-based processing and bilinear interpolation to eliminate block artifacts. We provide sample images demonstrating before/after comparisons to evaluate enhancement effectiveness. Furthermore, we address practical limitations such as computational intensity and noise amplification in homogeneous regions, while suggesting improvements like contrast-limited AHE (CLAHE) for optimized performance. This technical breakdown aims to provide researchers and developers with actionable insights for computer vision applications.