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Proceedings Paper • new

Color image denoising based on low-rank tensor train
Author(s): Yang Zhang; Zhi Han; Yandong Tang
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Paper Abstract

Tensor has been widely used in computer vision due to its ability to maintain spatial structure information. Owning to the well-balanced unfolding matrices, the recently proposed tensor train (TT) decomposition can make full use of information from tensors. Thereby, tensor train representation has a better performance in many fields compared to traditional methods of tensor decomposition. Inspired by the success of tensor train, in this paper, we firstly apply lowrank tensor train to recovering noisy color images. Meanwhile, we propose a novel algorithm for noise-contaminated images based on the block coordinate descent (BCD) method. The numerical experiments demonstrate that our algorithm can give a better result in the real color image both visually and numerically.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692P (6 May 2019); doi: 10.1117/12.2524189
Show Author Affiliations
Yang Zhang, Shenyang Institute of Automation (China)
Institutes for Robotics and Intelligent Manufacturing (China)
Univ. of Chinese Academy of Sciences (China)
Zhi Han, Shenyang Institute of Automation (China)
Institutes for Robotics and Intelligent Manufacturing (China)
Yandong Tang, Shenyang Institute of Automation (China)
Institutes for Robotics and Intelligent Manufacturing (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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