MATLAB Image Registration: Detailed Methods for Infrared and Visible Light Image Registration

MATLAB Image Registration: Detailed Methods for Infrared and Visible Light Image Registration

Image registration is a fundamental technique in computer vision and image processing domains. It aligns multiple images to achieve spatial consistency. For infrared and visible light image registration, we utilize MATLAB to accomplish this objective. Below is a detailed methodology for infrared-visible image registration:

1. Load infrared and visible light images into MATLAB using functions like imread() to import image data into the workspace.

2. Perform image preprocessing operations including noise reduction (using filters like medfilt2 or wiener2) and contrast enhancement (with histeq or adapthisteq) to improve registration accuracy.

3. Apply feature extraction algorithms such as SURF (detectSURFFeatures) or SIFT to identify distinctive keypoints in both infrared and visible light images.

4. Implement feature matching algorithms like matchFeatures with nearest-neighbor search to establish correspondences between extracted features from both modalities.

5. Compute transformation matrices (affine or projective using fitgeotrans) based on matched feature points to achieve spatial alignment between images.

6. Apply the calculated transformation matrix using imwarp function to register infrared and visible light images into a common coordinate system.

7. Perform post-processing operations including distortion correction (using imtransform with custom spatial transformations) and image restoration to enhance final output quality.

8. Save registered images with imwrite function for subsequent analysis and applications such as multisensor fusion or comparative studies.

By implementing this methodology, we can effectively achieve infrared-visible image registration, providing a foundation for advanced image analysis and multimodal applications.