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- Publisher Website: 10.1109/TIP.2019.2906491
- Scopus: eid_2-s2.0-85068451104
- PMID: 30908219
- WOS: WOS:000473641100006
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Article: Structural Similarity-Based Nonlocal Variational Models for Image Restoration
Title | Structural Similarity-Based Nonlocal Variational Models for Image Restoration |
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Authors | |
Keywords | regularization gradient nonlocal variational model Image restoration structural similarity index |
Issue Date | 2019 |
Citation | IEEE Transactions on Image Processing, 2019, v. 28, n. 9, p. 4260-4272 How to Cite? |
Abstract | © 1992-2012 IEEE. In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models. |
Persistent Identifier | http://hdl.handle.net/10722/276528 |
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 | Wang, Wei | - |
dc.contributor.author | Li, Fang | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:33:53Z | - |
dc.date.available | 2019-09-18T08:33:53Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2019, v. 28, n. 9, p. 4260-4272 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276528 | - |
dc.description.abstract | © 1992-2012 IEEE. In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | regularization | - |
dc.subject | gradient | - |
dc.subject | nonlocal variational model | - |
dc.subject | Image restoration | - |
dc.subject | structural similarity index | - |
dc.title | Structural Similarity-Based Nonlocal Variational Models for Image Restoration | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2019.2906491 | - |
dc.identifier.pmid | 30908219 | - |
dc.identifier.scopus | eid_2-s2.0-85068451104 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 4260 | - |
dc.identifier.epage | 4272 | - |
dc.identifier.isi | WOS:000473641100006 | - |
dc.identifier.issnl | 1057-7149 | - |