Aligning Multi-Sensor Images of Different Resolutions for Image Fusion: A Feature-Based and Mutual-Information-Based Registration Pipeline

Image fusion combines images from different sources into a single, more informative image and is used in panorama construction, remote sensing, medical imaging and infrared–visible surveillance, among other fields. The quality of the fused result depends directly on how accurately the input images have been geometrically aligned (registered) beforehand. This paper systematically presents two complementary approaches for aligning two images for fusion: (i) feature-based alignment for images from similar sensors (SIFT/SURF/ORB, ratio test, RANSAC, homography), and (ii) intensity-based alignment using mutual information (MI) for images acquired by different sensors at different resolutions. Preprocessing, transformation model selection, optimization, warping, fusion and post processing are organized into an end-to-end pipeline, and practical guidance is given on handling resolution differences in physical coordinates. The paper also discusses frequent implementation pitfalls in MI-based registration and appropriate evaluation measures. The pipeline is described reproducibly using the open-source OpenCV and SimpleITK libraries.