Registering a blurry IR image to a sharp RGB image in repetitive scenes is fundamentally a cross-modal + low-texture + ambiguity problem, so classical feature matching alone (SIFT/ORB + RANSAC) often fails.

A more robust approach is to combine multi-stage alignment + modality-invariant features:

1. Preprocessing (Critical for IR stability)

• Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) on IR image
• Use edge enhancement (Sobel / Laplacian) to reduce blur impact
• Optionally denoise using bilateral filter instead of Gaussian blur

2. Use Modality-Invariant Features

Instead of raw SIFT:

• Dense feature matching (SuperPoint / SuperGlue)
• D2-Net or R2D2 (more stable in repetitive patterns)
• Or edge-based descriptors (HOG / gradient maps)

These reduce dependence on texture uniqueness, which is the main failure point in repetitive structures.

3. Add Structural Constraints (Very Important)

For repeating scenes:

• Use keypoint clustering + spatial consistency filtering
• Enforce global geometric constraints (e.g., homography or affine consistency checks)
• Use RANSAC with stricter inlier thresholds + MAGSAC++ if available

4. Cross-Modal Registration Strategy

Best-performing pipeline in practice:

• Convert both IR and RGB → edge maps
• Match on edge representations instead of intensity
• Then refine alignment using:
  • ECC (Enhanced Correlation Coefficient maximization in OpenCV)
  • Or optical flow refinement (Farneback / RAFT)

5. Deep Learning Approach (Best Accuracy)

If you can use ML models:

• TransVGG / Deep Homography Networks
• RAFT-based cross-modal flow estimation
• Or pretrained multi-spectral registration networks

These handle:

• blur
• modality gap (IR vs RGB)
• repeated patterns better than handcrafted features

Practical RecommendationA strong hybrid pipeline is:

Edge-map conversion → SuperPoint matching → RANSAC/MAGSAC → ECC refinement

This combination is currently one of the most stable approaches for IR–RGB registration in real-world repetitive environments.