Towards efficient motion-blurred public security video super-resolution based on back-projection networks

摘要

In public security, video super-resolution has been an important technology for assisting users in effectively locating potential safety hazards. However, the video collected from video monitoring equipment is usually polluted by motion blur and noise, which results in low video quality after reconstruction. In this paper, we propose a super-resolution reconstruction model for motion-blurred video to reconstruct corresponding high resolution video from motion-blurred low-resolution video. First, a single-frame image super-resolution model is designed based on the improved deep back-projection network, which primarily includes the design of a recursive method. Second, a multi-frame image super-resolution model is designed based on an optical motion algorithm for motion estimation and motion compensation of video frame sequences. Finally, according to the motion-blurred low-resolution video pollution condition, the de-blurring algorithm and multi-frame image super resolution are combined to propose a super-resolution reconstruction model for motion-blurred video. Experiments show that the proposed motion-blurred video super-resolution model is superior to other contrasted algorithms in clear video and motion-blurred video reconstruction and has a better reconstruction effect and higher practical value.