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
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Popis
Shrnutí:	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 self-similarity (NSS); most existing works utilize one or two of them to implement completion. Naturally, there arises an interesting question: can one concurrently make use of multiple priors in a unified way, such that they can collaborate with each other to achieve better performance? This work gives a positive answer by formulating a novel tensor completion framework which can simultaneously take advantage of the global-local-nonlocal priors. In the proposed framework, the tensor train (TT) rank is adopted to characterize the global correlation; meanwhile, two Plug-and-Play (PnP) denoisers, including a convolutional neural network (CNN) denoiser and the color block-matching and 3 D filtering (CBM3D) denoiser, are incorporated to preserve local details and exploit NSS, respectively. Then, we design a proximal alternating minimization algorithm to efficiently solve this model under the PnP framework. Under mild conditions, we establish the convergence guarantee of the proposed algorithm. Extensive experiments show that these priors organically benefit from each other to achieve state-of-the-art performance both quantitatively and qualitatively.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1057-7149 1941-0042 1941-0042
DOI:	10.1109/TIP.2021.3138325