The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
Compressive video sensing (CVS) has great research significance in the video acquisition system with limited sampling resources. In this paper, we proposed a reconstruction algorithm based on total variation (TV) and nonlocal low-rank regularization (NLR-CS) to better reconstruct video signal from compressive sampled data. This algorithm consists two steps: The first step considers local correlation between and within video frames, applies TV as the prior constraint to obtain the initial recovered frame; In the second step, the improved NLR-CS algorithm is utilized to further reconstruct video frame considering the nonlocal self-similarity (NLSS). This step first blocks the initial recovered frame, finds similar blocks in the current frame and the key frames to construct low-rank matrix, then a low-ranking regularization reconstruction is performed. Experimental results show that the proposed algorithm can reconstruct video signals well, obtains higher video reconstruction accuracy than other CVS reconstruction algorithms.