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Total variation regularized low-rank tensor approximation for color image denoising
Yongyong Chen; Yicong Zhou
2019-01-17
Conference NameIEEE International Conference on Systems, Man, and Cybernetics (SMC)
Source Publication2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Pages2523-2527
Conference Date7-10 Oct. 2018
Conference PlaceMiyazaki, Japan, Japan
Abstract

Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a color image. To overcome these drawbacks, we propose a total variation regularized low-rank tensor approximation model for color image denoising. The model integrates the strong lowrank prior into a tensor-SVD framework, and introduces the hyper total variation to model the spatial smooth structure of images. Using the alternating direction method of multipliers, we propose a simple algorithm to solve our model. Extensive results on simulated and real noisy color images demonstrate the better performance of the proposed method against state-ofthe-art denoising methods

KeywordLow-rank Tensor Approximation Tensor-singular Value Decomposition Color Image Denoising
DOIhttp://doi.org/10.1109/SMC.2018.00432
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000459884802099
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science University of Macau, Macau 999078, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yongyong Chen,Yicong Zhou. Total variation regularized low-rank tensor approximation for color image denoising[C],2019:2523-2527.
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