Video restoration based on PatchMatch and reweighted low-rank matrix recovery

Abstract

In this paper, a new video restoration approach is proposed. By using a modified version of random PatchMatch algorithm, nearest-neighbor patches among the video frames can be grouped quickly and accurately. Then the video restoration problem can be boiled down to a low-rank matrix recovery problem, which is able to separate sparse errors from matrices that possess potential low-rank structures. Furthermore, the reweighted low-rank matrix model is used to improve the performance of video restoration by enhancing the sparsity of the sparse matrix and the low-rank property of the low-rank matrix. Experimental results show that our system achieves good performance in denosing of joint multi-frames and inpainting in the presence of small damaged areas.