Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

Bengua, JA; Phien, HN; Tuan, HD; Do, MN

Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

Bengua, JA

Phien, HN Tuan, HD

Do, MN

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2017, 26 (5), pp. 2466 - 2479
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Bengua, JA https://orcid.org/0000-0001-9375-0449	en_US
dc.contributor.author	Phien, HN	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Do, MN	en_US
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2017, 26 (5), pp. 2466 - 2479	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/45784
dc.description.abstract	© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2017.2672439	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.

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http://hdl.handle.net/10453/45784