Image Registration Using Adaptive Regularization Method with MATLAB Implementation

This MATLAB code demonstration implements image registration using an adaptive regularization approach, accompanied by test image datasets for practical experimentation. Image registration represents a fundamental image processing technique that aligns two or more images to enable subsequent analysis and processing operations. The adaptive regularization method serves as a widely adopted algorithm that automatically adjusts image brightness and contrast parameters to achieve superior registration accuracy. The implementation includes core functionalities such as: - Intensity-based similarity measurement using normalized cross-correlation - Multi-scale optimization framework for robust convergence - Automatic regularization parameter adjustment based on image gradient analysis - Bidirectional transformation mapping with cubic interpolation Through this MATLAB example, users can comprehensively study and understand the practical application of adaptive regularization methods in image registration. The provided test datasets allow for direct validation of code functionality and performance evaluation under various imaging conditions. This resource serves as both an educational tool and a practical reference for implementing advanced image alignment techniques. Key functions featured in the code: 1. adaptiveRegularization() - Main algorithm implementation with parameter optimization 2. imagePreprocessing() - Handles intensity normalization and feature enhancement 3. transformEstimation() - Computes spatial transformations using gradient descent 4. resultsEvaluation() - Quantifies registration accuracy through metric calculations We anticipate this demonstration will provide valuable insights into adaptive image registration methodologies and facilitate further development of computer vision applications.