MATLAB Image Registration: Algorithm Implementation with Source Code

This article presents a MATLAB-based image registration algorithm along with its complete source code implementation. The algorithm supports various image formats including JPEG, PNG, and BMP, utilizing MATLAB's built-in functions like imread() for format-agnostic image loading and imregister() for optimized spatial transformation. Image registration is a fundamental technique for aligning two or more images to enable comparative analysis and processing. The implementation incorporates key MATLAB functions such as: - imregconfig() for configuring intensity-based registration parameters - fitgeotrans() for estimating geometric transformations - imwarp() for applying transformation matrices to input images This technology finds extensive applications across multiple domains including medical imaging (for MRI/CT scan alignment), computer vision (for object tracking and scene reconstruction), and astronomy (for celestial image stacking). The code structure includes modular functions for feature detection, transformation estimation, and image warping, allowing users to customize parameters based on specific requirements. The article provides detailed explanations of the core algorithm workflow: feature extraction using corner detection methods (like detectSURFFeatures()), transformation matrix calculation through optimization techniques, and resampling implementation using interpolation methods. Practical examples demonstrate the algorithm's effectiveness in handling rotation, scaling, and translation adjustments between reference and target images. Whether you're a professional researcher requiring robust image alignment solutions or a beginner exploring computer vision techniques, this resource offers practical implementation guidance with commented code segments and parameter tuning recommendations. The complete MATLAB source code includes error handling for invalid inputs and performance optimization tips for large-scale image processing. All code examples are structured with clear variable naming conventions and include detailed comments explaining each processing stage, from initial image preprocessing to final alignment validation using metrics like mean squared error (MSE) calculation.