Mutual Information Implementation for Image Registration

This implementation utilizes mutual information to achieve precise image registration functionality. Mutual information serves as a statistical measure that quantifies the dependency between two random variables, making it particularly valuable for multimodal data alignment. In practical implementation, the algorithm typically involves:

Key computational steps include: 1. Calculating joint histogram distributions between reference and floating images 2. Estimating marginal and joint probability density functions using Parzen windowing or histogram techniques 3. Maximizing mutual information through optimization methods like gradient descent or Powell's algorithm

Through mutual information maximization, the system identifies optimal transformation parameters (rotation, translation, scaling) that align similar features across images. This approach significantly enhances accuracy in medical imaging, remote sensing, and computer vision applications by effectively handling intensity variations between different imaging modalities.