Panorama recovery from noisy UAV surveillance video

This paper proposes an efficient and robust algorithm to recover a panorama from poorly-obtained UAV video frames contaminated with significant noise. In this algorithm, the eigen-space based neighborhood region will be introduced with our novel feature-based random M least-squares (RMLS) registration technique. Meanwhile, the corresponding similarity regions will be assigned weights according to the relativity between these neighboring regions. Next, Bayesian multi-frame sampling will be implemented utilizing the homography estimated by the frame registration. Finally, the sub-region in each frame which is applicable to the multi-frame sampling will be stitched utilizing multi-resolution blending.