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.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61272028, 61473317, 61273274); Fundamental Research Funds for the Central Universities of China (Grant No. 2013JBZ003); Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20120009110008); Program for New Century Excellent Talents in University (Grant No. NCET-12-0768); National Key Technology R&D Program of China (Grant No. 2012BAH01F03); National Basic Research (973) Program of China (Grant No. 2011CB302203); The National High Technology Research and Development Program (863) of China (Grant No. 2014AA015202); Program for Changjiang Scholars and Innovative Research Team in University (Grant no. IRT201206).
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Xu, BH., Cen, YG., Wei, Z. et al. Video restoration based on PatchMatch and reweighted low-rank matrix recovery. Multimed Tools Appl 75, 2681–2696 (2016). https://doi.org/10.1007/s11042-015-2545-1
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DOI: https://doi.org/10.1007/s11042-015-2545-1