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.

在公共安全中，视频超分辨率已成为帮助用户有效定位潜在安全隐患的一项重要技术。但是，从视频监控设备收集的视频通常会被运动模糊和噪声污染，从而导致重建后的视频质量低下。在本文中，我们提出了一种用于运动模糊视频的超分辨率重建模型，以从运动模糊的低分辨率视频中重建相应的高分辨率视频。首先，基于改进的深度反投影网络，设计了单帧图像超分辨率模型，该模型主要包括递归方法的设计。其次，基于光学运动算法设计了多帧图像超分辨率模型，用于视频帧序列的运动估计和运动补偿。最后，根据运动模糊的低分辨率视频污染状况，结合去模糊算法和多帧图像超分辨率，提出了运动模糊视频的超分辨率重建模型。实验表明，所提出的运动模糊视频超分辨率模型在清晰视频和运动模糊视频的重建中优于其他对比算法，具有较好的重建效果和较高的实用价值。