Image Registration Based on Mutual Information with Powell Optimization

Mutual information-based image registration is a widely used image processing technique. In this approach, mutual information serves as the similarity metric to evaluate the alignment between two images. The optimization process employs Powell's algorithm, an efficient nonlinear optimization method particularly suitable for multi-dimensional parameter spaces without requiring gradient calculations. The implementation uses MATLAB programming, leveraging functions like imregconfig for registration configuration and custom mutual information calculation routines. Through this registration technique, we achieve enhanced spatial alignment between images, enabling more accurate subsequent image processing and analysis operations such as image fusion, change detection, and quantitative comparisons. The algorithm typically involves histogram-based mutual information computation, affine transformation parameter optimization, and iterative interpolation operations to maximize the similarity metric.