Maximum Entropy Restoration with MATLAB Implementation

This repository presents a MATLAB implementation of maximum entropy restoration algorithm. The code is ready-to-run and contains extensive in-line documentation explaining each computational step. The implementation employs probability distribution optimization techniques to maximize entropy while satisfying image constraints, utilizing MATLAB's optimization toolbox for efficient convergence. A key feature includes comparative analysis modules that allow side-by-side performance evaluation with Wiener filtering (frequency-domain approach) and Blind LUCY deconvolution methods. These comparisons demonstrate trade-offs in noise handling, edge preservation, and computational efficiency through quantitative metrics and visual results. For beginners in image processing, the code provides practical insights into entropy maximization principles, iterative optimization techniques, and objective comparison methodologies between different restoration approaches. The program serves as an educational tool to understand both theoretical concepts and practical implementation challenges in maximum entropy restoration.