Video denoising using low rank tensor decomposition

Lihua Gui; Gaochao Cui; Qibin Zhao; Dongsheng Wang; Andrzej Cichocki; Jianting Cao

doi:10.1117/12.2268435

17 March 2017 Video denoising using low rank tensor decomposition

Lihua Gui, Gaochao Cui, Qibin Zhao, Dongsheng Wang, Andrzej Cichocki, Jianting Cao

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410V (2017) https://doi.org/10.1117/12.2268435
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Lihua Gui, Gaochao Cui, Qibin Zhao, Dongsheng Wang, Andrzej Cichocki, and Jianting Cao "Video denoising using low rank tensor decomposition", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410V (17 March 2017); https://doi.org/10.1117/12.2268435

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