MATLAB Implementation of Image Binarization and Processing Techniques

Image Binarization, Discrete Noise Removal, Color Image Grayscale Conversion, Feature Extraction, Grayscaling Methods, and Average Grayscale Calculation for Color Images

RMSHE Algorithm (Recursive Mean-Separate Histogram Equalization)

RMSHE algorithm (Recursive Mean-Separate Histogram Equalization). Core principle involves segmenting images based on mean grayscale values and performing histogram equalization on each segment separately. The package includes MATLAB source code implementation, research paper documentation, and input test images for comprehensive evaluation.

Iterative Optimal Threshold Segmentation

Iterative Optimal Threshold Segmentation - The algorithm begins by selecting the initial threshold as the average grayscale value T0 of the image. It then partitions the image pixels into two groups using T0, calculates the average grayscale values for each group (TA for pixels below T0, TB for pixels above T0), and computes a new threshold T1 as the average of TA and TB. This process iterates recursively, updating the threshold at each step until convergence is achieved when TK+1 equals TK. The method demonstrates how iterative computation can progressively refine the segmentation boundary for optimal image partitioning.