Please use this identifier to cite or link to this item: https://doi.org/10.1137/070703533
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dc.titleA fast total variation minimization method for image restoration
dc.contributor.authorHuang, Y.
dc.contributor.authorNg, M.K.
dc.contributor.authorWen, Y.-W.
dc.date.accessioned2014-12-12T07:09:30Z
dc.date.available2014-12-12T07:09:30Z
dc.date.issued2008
dc.identifier.citationHuang, Y., Ng, M.K., Wen, Y.-W. (2008). A fast total variation minimization method for image restoration. Multiscale Modeling and Simulation 7 (2) : 774-795. ScholarBank@NUS Repository. https://doi.org/10.1137/070703533
dc.identifier.issn15403459
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/114978
dc.description.abstractIn this paper, we study a fast total variation minimization method for image restoration. In the proposed method, we use the modified total variation minimization scheme to denoise the deblurred image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. Our experimental results show that the quality of restored images by the proposed method is competitive with those restored by the existing total variation restoration methods. We show the convergence of the alternating minimization algorithm and demonstrate that thealgorithm is very efficient. © 2008 Society for Industrial and applied Mathematics.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1137/070703533
dc.sourceScopus
dc.subjectDeblurring
dc.subjectDenoising
dc.subjectImage restoration
dc.subjectTotal variation
dc.typeArticle
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1137/070703533
dc.description.sourcetitleMultiscale Modeling and Simulation
dc.description.volume7
dc.description.issue2
dc.description.page774-795
dc.identifier.isiut000260847400012
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