Standard image denoising algorithms often rely on data-loss techniques. While fast, they perform poorly in low signal-to-noise ratio scenarios like astrophotography. Stacking-based algorithms resolve this by utilizing noise variability across multiple frames, but they require precise image alignment to prevent artifacts.

In this paper, we propose M-BRISK, a modified BRISK feature detection and description algorithm offering improved robustness and accuracy. A novel keypoint-detection strategy with efficient filtering contributes to this enhanced performance. Crucially, M-BRISK targets uniform spatial distribution of both keypoints and matched descriptor pairs across the image, achieved by partitioning the image into regions and enforcing balanced selection within each. This ensures that the estimated homography is informed by correspondences spread across the entire image rather than clustered in salient areas, leading to more reliable homography estimation using RANSAC.

Evaluation across four test images and four synthetic homographies simulating realistic camera displacements, as well as rotation invariance tests from 0° to 180°, demonstrate that M-BRISK achieves homography estimation accuracy superior to BRISK by 15.7% and to SIFT by 13.1%, while also exhibiting better average rotation invariance. Furthermore, M-BRISK maintains stable processing speeds regardless of keypoint count. While BRISK's performance degrades as detections increase, M-BRISK becomes substantially faster beyond 15,000 keypoints. As a result, M-BRISK enables fast, high-quality image registration, making it well-suited for subsequent denoising through stacking.