Abstract
This paper proposes a new algorithm based on low-rank matrix recovery to remove salt & pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt & pepper noise from a video tracking sequence as a low-rank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.
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Yongxia Zhang received the B.S. degree from Qilu University of Technology, Jinan, China, in 2011. She is currently working toward the Ph.D. degree in the School of Computer Science and Technology, Shandong University, Jinan, China. Her research interests include computer vision, image processing, and machine learning. In particular, she focuses on the problems of image and video denoising and image segmentation.
Yi Liu received the M.S. degree in electronic information science and technology from the Information Engineering College, Shandong University in 1998, the Ph.D. degree in computer software and theory of computer science and technology from Shandong University in 2006. She was a postdoctoral fellow of computer science and technology in Peking University in 2008. Currently, she is a professor in the School of Computer Science and Technology, Shandong University. Her research interests include wavelets, image signal processing, and pattern recognition.
Xuemei Li received the M.S. and Ph.D. degrees from Shandong University, Jinan, China, in 2004 and 2010, respectively. She is currently an associate professor in School of Computer Science and Technology, Shandong University. She is engaged in research on geometric modeling, CAGD, medical image processing, and information visualization.
Caiming Zhang is a professor and doctoral supervisor of the School of Computer Science and Technology, Shandong University. He received the B.S. and M.E. degrees in computer science from Shandong University in 1982 and 1984, respectively, and a D.E. degree in computer science from the Tokyo Institute of Technology, Japan, in 1994. From 1997 to 2000, Dr. Zhang has held the visiting position in the University of Kentucky, USA. His research interests include CAGD, CG, information visualization, and medical image processing.
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Zhang, Y., Liu, Y., Li, X. et al. Salt and pepper noise removal in surveillance video based on low-rank matrix recovery. Comp. Visual Media 1, 59–68 (2015). https://doi.org/10.1007/s41095-015-0005-5
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DOI: https://doi.org/10.1007/s41095-015-0005-5