Image Registration Using Mutual Information with Particle Swarm Optimization

Source code for image registration based on mutual information, implementing the particle swarm optimization (PSO) algorithm. This approach utilizes mutual information as a similarity metric to evaluate the alignment between images, enabling accurate image registration. The implementation includes calculating joint probability distributions between images and optimizing transformation parameters using mutual information as the objective function. Particle swarm optimization is a heuristic algorithm that mimics the social behavior of bird flocking to search for optimal solutions. In this implementation, particles represent potential transformation parameters (such as rotation, translation, and scaling), and their velocities are updated based on individual and global best positions. The algorithm efficiently explores the parameter space to maximize mutual information between reference and moving images. The combination of these two techniques creates a robust and effective image registration method suitable for various applications. Key implementation features include adaptive parameter tuning, multi-resolution registration strategies, and efficient computation of mutual information gradients for faster convergence. This approach demonstrates excellent performance in medical imaging, remote sensing, and computer vision tasks where precise image alignment is crucial.