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Article: Total variation structured total least squares method for image restoration
Title | Total variation structured total least squares method for image restoration |
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Authors | |
Keywords | Image restoration Structured total least squares Regularization Total variation Alternating minimization |
Issue Date | 2013 |
Citation | SIAM Journal on Scientific Computing, 2013, v. 35, n. 6, p. B1304-B1320 How to Cite? |
Abstract | In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.Copyright © by SIAM. |
Persistent Identifier | http://hdl.handle.net/10722/276975 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 1.803 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhao, Xi Le | - |
dc.contributor.author | Wang, Wei | - |
dc.contributor.author | Zeng, Tie Yong | - |
dc.contributor.author | Huang, Ting Zhu | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:35:13Z | - |
dc.date.available | 2019-09-18T08:35:13Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | SIAM Journal on Scientific Computing, 2013, v. 35, n. 6, p. B1304-B1320 | - |
dc.identifier.issn | 1064-8275 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276975 | - |
dc.description.abstract | In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.Copyright © by SIAM. | - |
dc.language | eng | - |
dc.relation.ispartof | SIAM Journal on Scientific Computing | - |
dc.subject | Image restoration | - |
dc.subject | Structured total least squares | - |
dc.subject | Regularization | - |
dc.subject | Total variation | - |
dc.subject | Alternating minimization | - |
dc.title | Total variation structured total least squares method for image restoration | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1137/130915406 | - |
dc.identifier.scopus | eid_2-s2.0-84892563008 | - |
dc.identifier.volume | 35 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | B1304 | - |
dc.identifier.epage | B1320 | - |
dc.identifier.eissn | 1095-7200 | - |
dc.identifier.isi | WOS:000330028400032 | - |