Photo Mosaicking of Low-Contrast Underwater Images

Sat, 05 Oct 2024 00:00:00 +0000

This project implements a full photo mosaicking and optimization pipeline using low-contrast underwater images from the Skerki Bank Roman shipwreck dataset. The approach registers both sequential and non-sequential images using SIFT and RANSAC, computes affine transformations, and optimizes a global trajectory using GTSAM.

Pipeline Breakdown

CLAHE Image Enhancement

Applies histogram equalization to improve contrast and enhance keypoints.
OpenCV CLAHE was used on each grayscale frame.

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
clahe_image = clahe.apply(gray_image)

SIFT Feature Detection

Detected keypoints using tuned SIFT settings:
- nfeatures=5000, contrastThreshold=0.025, nOctaveLayers=8, sigma=1.5

sift = cv2.SIFT_create(...)
kp, desc = sift.detectAndCompute(image, None)

Feature Matching + RANSAC Filtering

Matched descriptors using Brute-Force Matcher + Lowe’s ratio test.
Applied cv2.estimateAffine2D with RANSAC to compute and refine transformation.

matches = bf.knnMatch(des1, des2, k=2)
good = [m for m, n in matches if m.distance < 0.75 * n.distance]
H, mask = cv2.estimateAffine2D(pts1, pts2, method=cv2.RANSAC)

Pose Graph Construction (GTSAM)

Built a factor graph using all non-repeating image pairs.
Relative poses (affine transforms) were added as edges.

graph.add(BetweenFactorPose2(i1, i2, T_ij, noise_model))

Global Bundle Adjustment

Used GTSAM’s Levenberg-Marquardt optimizer to refine global poses.
Corrects drift and adjusts poses to minimize total residual error.

optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate)
result = optimizer.optimize()

Techniques Used

Image normalization + CLAHE
SIFT feature detection and matching
RANSAC for outlier rejection
Homography estimation using Levenberg–Marquardt
Graph construction (GTSAM)
Loop closure detection
Pose optimization

Results

Successfully registered both sequential and non-sequential image pairs
Constructed optimized pose graphs for 6 and 29 image subsets
Achieved a ~20% improvement in alignment after bundle adjustment

🔗 GitHub Repository
📁 Skerki Dataset Reference (Google Drive)
📓 Project Notebook
📓 Extended Analysis

References

Pizarro & Singh (2003): Toward large-area mosaicing for underwater scientific applications.
Ballard et al. (1998, 2000): Roman shipwreck discovery using submersible tech.

Levenberg-Marquardt | Saikiran Juttu | Robotics Portfolio