MATLAB Implementation of Image Registration with Mutual Information and PSO Optimization

This document presents a MATLAB implementation of an image registration program optimized using mutual information and Particle Swarm Optimization (PSO) techniques. The program employs mutual information as a similarity metric to effectively measure the correspondence between two images, significantly reducing registration errors during the alignment process. The implementation calculates mutual information through probability distribution analysis of image intensities, typically using histogram-based methods or Parzen window estimation for continuous distributions. PSO serves as a heuristic optimization algorithm that efficiently explores the parameter space (including translation, rotation, and scaling transformations) to identify optimal registration parameters. The MATLAB code structure includes key functions for image preprocessing, transformation parameter initialization, mutual information computation, and PSO-based optimization loops. We believe this program can significantly contribute to the image registration field, enabling researchers and engineers to effectively apply registration techniques to solve practical problems in medical imaging, remote sensing, and computer vision applications.