Super-resolution mosaicking of UAV surveillance video

This paper explains and implements our efficient multi-frame super-resolution mosaicking algorithm. In this algorithm, feature points between images are matched using SIFT, and then random M-least squares is used to estimate the homography between frames. Next, separate frames are registered and the overlapping region is extracted. A generative model is then adopted and combined with maximum a posteriori estimation to construct the underdetermined sparse linear system. To solve the ill-posed large-scale inverse system, we derive and implement a new hybrid regularization (bilateral total variance Hubert) method. Cross validation is utilized to estimate the derivative of the blur kernel as well as the regularization parameter. Super-resolution is then applied to the individual sub-frames from the overlapping region. Finally, multi-band blending is used to stitch these resolution-enhanced frames to form the final image. The whole process is semi-real time (roughly 30 seconds for 35 frames) and the effectiveness of our algorithm is validated by applying it to real and synthetic UAV video frames.