Tensor Completion via Complementary Global, Local, and Nonlocal Priors

Completing missing entries in multidimensional visual data is a typical ill-posed problem that requires appropriate exploitation of prior information of the underlying data. Commonly used priors can be roughly categorized into three classes: global tensor low-rankness, local properties, and nonlocal...

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Vydáno v:	IEEE transactions on image processing Ročník 31; s. 984 - 999
Hlavní autoři:	Zhao, Xi-Le, Yang, Jing-Hua, Ma, Tian-Hui, Jiang, Tai-Xiang, Ng, Michael K., Huang, Ting-Zhu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms alternating direction method of multipliers Artificial neural networks color block-matching and 3 D filtering Color matching convolutional neural network Convolutional neural networks Correlation Hyperspectral imaging Ill posed problems Mathematical analysis Matrix decomposition Minimization plug-and-play proximal alternating minimization Self-similarity Tensor train rank Tensors Visualization
ISSN:	1057-7149, 1941-0042, 1941-0042
On-line přístup:	Získat plný text
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