MATLAB Implementation of Subpixel Image Registration Based on Cross-Correlation with Source Code

MATLAB implementation of subpixel-level image registration based on cross-correlation with complete source code. Image registration refers to the accurate alignment of multiple images for subsequent image processing and analysis. Cross-correlation is a commonly used image registration method that achieves precise alignment by calculating similarity between images. The algorithm computes normalized cross-correlation coefficients to identify optimal translation parameters between reference and target images. Subpixel registration performs finer alignment beyond pixel-level precision, significantly improving registration accuracy. The implementation utilizes interpolation techniques and peak localization methods to achieve subpixel resolution. This source code, developed in MATLAB programming language, implements a cross-correlation based subpixel image registration algorithm featuring: - Normalized cross-correlation computation for robust similarity measurement - Peak detection algorithms for precise displacement estimation - Subpixel interpolation methods (such as parabolic fitting or frequency domain techniques) - Automated parameter optimization for different image types Key functions include image preprocessing, correlation matrix calculation, peak identification, and subpixel refinement routines. The code provides researchers and practitioners with a practical tool for image registration studies and applications, supporting various medical, remote sensing, and computer vision scenarios.