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Proceedings Paper

Video denoising using low rank tensor decomposition
Author(s): Lihua Gui; Gaochao Cui; Qibin Zhao; Dongsheng Wang; Andrzej Cichocki; Jianting Cao
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Paper Abstract

Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410V (17 March 2017); doi: 10.1117/12.2268435
Show Author Affiliations
Lihua Gui, Saitama Institute of Technology (Japan)
RIKEN Brain Science Institute (Japan)
Gaochao Cui, Saitama Institute of Technology (Japan)
RIKEN Brain Science Institute (Japan)
Qibin Zhao, Saitama Institute of Technology (Japan)
RIKEN Brain Science Institute (Japan)
Dongsheng Wang, Saitama Institute of Technology (Japan)
Andrzej Cichocki, RIKEN Brain Science Institute (Japan)
Jianting Cao, Saitama Institute of Technology (Japan)
RIKEN Brian Science Institute (Japan)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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