Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high comput...

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Vydané v:	IEEE transactions on image processing Ročník 33; s. 2835 - 2850
Hlavní autori:	Qin, Wenjin, Wang, Hailin, Zhang, Feng, Ma, Weijun, Wang, Jianjun, Huang, Tingwen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	ADMM algorithm Algorithms Approximation Approximation algorithms Computational modeling Computing costs High-order T-SVD framework Image reconstruction Indexes Mathematical analysis nonconvex regularizers Optimization randomized low-rank tensor approximation robust high-order tensor completion Robustness Singular value decomposition Tensors
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line prístup:	Získať plný text
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