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Color Image Denoising Based on Low-Rank Tensor Train
Zhang Y(张杨)1,2,3; Han Z(韩志)1,2; Tang YD(唐延东)1,2
Department机器人学研究室
Conference Name2018 10th International Conference on Graphics and Image Processing (ICGIP 2018)
Conference DateDecember 12-14, 2018
Conference PlaceChengdu, China
Source Publication2018 10th International Conference on Graphics and Image Processing (ICGIP 2018)
PublisherSPIE
Publication PlaceBellingham, USA
2018
Pages1-6
Indexed ByEI ; CPCI(ISTP)
EI Accession number20192106949157
WOS IDWOS:000485096200095
Contribution Rank1
ISSN0277-786X
ISBN978-1-5106-2828-1
KeywordImage denoising low rank tensor train tensor networks color image
AbstractTensor 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 low-rank 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
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23847
Collection机器人学研究室
Corresponding AuthorZhang Y(张杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China
3.University of Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
Zhang Y,Han Z,Tang YD. Color Image Denoising Based on Low-Rank Tensor Train[C]. Bellingham, USA:SPIE,2018:1-6.
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