I’m working on an image registration problem where I need to align a low-quality, blurry infrared (IR) image with a high-resolution RGB image of the same scene.

The challenge is that the scene contains repeating structural patterns, which causes traditional feature matching (SIFT / ORB) to produce incorrect correspondences. Also, the IR image is significantly blurred and lower contrast, making keypoint detection unstable.

I’ve tried basic OpenCV approaches like SIFT + FLANN, but the matches are inconsistent due to ambiguity in repetitive regions.

Current Code Attempt (Python + OpenCV)

import cv2
import numpy as np

# Load images
rgb = cv2.imread("rgb.png")
ir = cv2.imread("ir.png", cv2.IMREAD_GRAYSCALE)

# Convert RGB to grayscale for matching
rgb_gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)

# Feature detector
sift = cv2.SIFT_create()

# Detect keypoints and descriptors
kp1, des1 = sift.detectAndCompute(ir, None)
kp2, des2 = sift.detectAndCompute(rgb_gray, None)

# FLANN matcher
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)

flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# Lowe's ratio test
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

print(f"Good matches found: {len(good_matches)}")

# Homography (fails often due to wrong matches)
if len(good_matches) > 10:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)

    H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

    aligned_ir = cv2.warpPerspective(ir, H, (rgb.shape[1], rgb.shape[0]))
else:
    print("Not enough reliable matches")
Problem

• Repeating structures cause ambiguous matches
• IR image blur reduces feature quality
• RANSAC still fails to find a stable homography in many cases

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