Detailed Methods for Infrared-Visible Image Registration using MATLAB

Infrared and visible light image registration is a key technology in computer vision and remote sensing fields. Due to different imaging principles between the two modalities, direct registration poses significant challenges. MATLAB provides extensive toolkits to support this task. Below are detailed methods and implementation approaches: ### 1. Image Preprocessing Given the substantial differences between infrared and visible light imaging, preprocessing is essential to enhance feature consistency. Common methods include: Histogram Equalization: Enhances image contrast to make features more prominent using `histeq` function Edge Enhancement: Strengthens contour information through Sobel or Canny operators (`edge` function with different method parameters) Noise Suppression: Eliminates sensor noise using Gaussian filtering (`imgaussfilt`) or median filtering (`medfilt2`) ### 2. Feature Extraction and Matching Key implementation steps: SIFT/SURF Feature Detection: Extract scale and rotation invariant feature points using MATLAB's `detectSURFFeatures` function, which returns keypoint locations and scales Feature Descriptor Matching: Calculate feature similarity through `matchFeatures` function, supporting Euclidean distance or Hamming distance metrics Mismatch Removal: Apply RANSAC algorithm to eliminate outliers and improve registration robustness using `estimateGeometricTransform` with 'ransac' method ### 3. Transformation Matrix Estimation Calculate spatial transformation relationships based on matched point pairs. Common models include: Affine Transformation: Suitable for scenarios with minor viewpoint changes, implemented using `fitgeotrans` function with 'affine' transform type Projective Transformation (Homography Matrix): Handles scenes with significant perspective differences using 'projective' transform type Polynomial Transformation: Used for non-linear distortion correction with 'polynomial' option and specified degree ### 4. Image Resampling and Fusion Image Transformation: Map infrared images to visible light coordinate system using `imwarp` function with calculated transformation matrix Resampling Methods: Balance accuracy and efficiency using bilinear interpolation ('linear') or cubic convolution interpolation ('cubic') Fusion Visualization: Display registration results through weighted superposition or pseudo-color mapping using `imfuse` function ### 5. Accuracy Evaluation Root Mean Square Error (RMSE): Calculate coordinate deviations of matched point pairs using coordinate difference computations Structural Similarity (SSIM): Assess structural preservation degree of registered images using `ssim` function for quantitative evaluation Important Considerations: For low-texture scenes, consider alternative methods like phase correlation or mutual information approach For multimodal registration, combine with deep learning methods (such as VGG features) to enhance performance using transfer learning techniques Through the above workflow, MATLAB can achieve accurate cross-modal alignment from millimeter-wave radar to optical images, applicable to complex scenarios like military reconnaissance and medical imaging.