An Optimized Image Registration Algorithm

This document proposes an optimized image registration algorithm. The implementation begins by extracting interest points and their orientation information from input images using feature detection techniques such as SIFT (Scale-Invariant Feature Transform) or ORB (Oriented FAST and Rotated BRIEF). The algorithm then employs mutual information criteria to compute corresponding point pairs, which involves calculating statistical dependencies between image regions to establish accurate matches. This process enables natural stitching of two or more images into a complete composite through transformation estimation and image warping operations. Experimental validation confirms the algorithm's effectiveness across multiple datasets, demonstrating robust performance under various conditions. Furthermore, we provide detailed experimental analysis and result discussions to comprehensively validate the algorithm's performance metrics including precision, recall, and computational efficiency. The method achieves excellent registration results on diverse datasets, proving its robustness and general applicability. Future research directions may focus on optimizing computational efficiency through parallel processing or GPU acceleration, enhancing precision via deep learning approaches, and expanding applications to broader image registration scenarios such as medical imaging or remote sensing.