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dc.contributor.authorChristiansen, Oddvareng
dc.contributor.authorLee, Tin-Maneng
dc.contributor.authorLie, Johaneng
dc.contributor.authorSinha, Ushaeng
dc.contributor.authorChan, Tony F.eng
dc.date.accessioned2008-10-22T12:21:23Z
dc.date.available2008-10-22T12:21:23Z
dc.date.issued2007eng
dc.identifier.urihttps://hdl.handle.net/1956/2822
dc.description.abstractWe generalize the total variation restoration model, introduced by Rudin, Osher, and Fatemi in 1992, to matrix-valued data, in particular, to diffusion tensor images (DTIs). Our model is a natural extension of the color total variation model proposed by Blomgren and Chan in 1998. We treat the diffusion matrix D implicitly as the product D = LLT , and work with the elements of L as variables, instead of working directly on the elements of D. This ensures positive definiteness of the tensor during the regularization flow, which is essential when regularizing DTI.We perform numerical experiments on both synthetical data and 3D human brain DTI, and measure the quantitative behavior of the proposed model.en_US
dc.language.isoengeng
dc.publisherHindawien_US
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/eng
dc.titleTotal Variation Regularization of Matrix-Valued Imagesen_US
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.rights.holderOddvar Christiansen et al.en_US
dc.identifier.doihttps://doi.org/10.1155/2007/27432
dc.identifier.cristin372165
dc.source.journalInternational Journal of Biomedical Imaging
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410nob


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