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- Publisher Website: 10.1109/TIP.2014.2329449
- Scopus: eid_2-s2.0-84904317424
- PMID: 24951688
- WOS: WOS:000340094000002
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Article: Compressive sensing via nonlocal low-rank regularization
Title | Compressive sensing via nonlocal low-rank regularization |
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Authors | |
Keywords | alternative direction multiplier method. Compresses sensing low-rank approximation nonconvex optimization structured sparsity |
Issue Date | 2014 |
Citation | IEEE Transactions on Image Processing, 2014, v. 23, n. 8, p. 3618-3632 How to Cite? |
Abstract | Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex logdet(X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery. © 2014 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/327008 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Dong, Weisheng | - |
dc.contributor.author | Shi, Guangming | - |
dc.contributor.author | Li, Xin | - |
dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Huang, Feng | - |
dc.date.accessioned | 2023-03-31T05:28:07Z | - |
dc.date.available | 2023-03-31T05:28:07Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2014, v. 23, n. 8, p. 3618-3632 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/327008 | - |
dc.description.abstract | Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex logdet(X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery. © 2014 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | alternative direction multiplier method. | - |
dc.subject | Compresses sensing | - |
dc.subject | low-rank approximation | - |
dc.subject | nonconvex optimization | - |
dc.subject | structured sparsity | - |
dc.title | Compressive sensing via nonlocal low-rank regularization | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2014.2329449 | - |
dc.identifier.pmid | 24951688 | - |
dc.identifier.scopus | eid_2-s2.0-84904317424 | - |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 3618 | - |
dc.identifier.epage | 3632 | - |
dc.identifier.isi | WOS:000340094000002 | - |