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Article: Total variation structured total least squares method for image restoration

TitleTotal variation structured total least squares method for image restoration
Authors
KeywordsImage restoration
Structured total least squares
Regularization
Total variation
Alternating minimization
Issue Date2013
Citation
SIAM Journal on Scientific Computing, 2013, v. 35, n. 6, p. B1304-B1320 How to Cite?
AbstractIn 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 Identifierhttp://hdl.handle.net/10722/276975
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 1.803
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhao, Xi Le-
dc.contributor.authorWang, Wei-
dc.contributor.authorZeng, Tie Yong-
dc.contributor.authorHuang, Ting Zhu-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:35:13Z-
dc.date.available2019-09-18T08:35:13Z-
dc.date.issued2013-
dc.identifier.citationSIAM Journal on Scientific Computing, 2013, v. 35, n. 6, p. B1304-B1320-
dc.identifier.issn1064-8275-
dc.identifier.urihttp://hdl.handle.net/10722/276975-
dc.description.abstractIn 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.languageeng-
dc.relation.ispartofSIAM Journal on Scientific Computing-
dc.subjectImage restoration-
dc.subjectStructured total least squares-
dc.subjectRegularization-
dc.subjectTotal variation-
dc.subjectAlternating minimization-
dc.titleTotal variation structured total least squares method for image restoration-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1137/130915406-
dc.identifier.scopuseid_2-s2.0-84892563008-
dc.identifier.volume35-
dc.identifier.issue6-
dc.identifier.spageB1304-
dc.identifier.epageB1320-
dc.identifier.eissn1095-7200-
dc.identifier.isiWOS:000330028400032-

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