RMSHE Algorithm (Recursive Mean-Separate Histogram Equalization)

The RMSHE algorithm (Recursive Mean-Separate Histogram Equalization) operates by recursively dividing images based on their mean intensity values, followed by independent histogram equalization of each resulting segment. The primary objective of this algorithm is to enhance image contrast and improve visual quality. Specifically, RMSHE employs an iterative approach that partitions the image into multiple regions, applying histogram equalization to the grayscale distribution within each region. This methodology offers significant advantages in preserving fine details and texture information, resulting in clearer and more vivid image representations. The algorithm implementation typically involves multiple processing stages, including image preprocessing operations (such as intensity normalization) and postprocessing steps (like contrast limitation) to further optimize output quality. The MATLAB implementation provides functions for calculating mean intensity thresholds, recursive image partitioning, and separate histogram equalization operations for each sub-region. Key components include a recursive division function that splits images based on dynamic mean calculations and a histogram processing module that handles equalization with controllable parameters. The package includes complete MATLAB source code with detailed implementation comments, supporting research papers explaining the mathematical foundation, sample input images for testing, and comprehensive documentation covering algorithm workflow with example results to facilitate better understanding and practical application of the technique.