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dc.contributor.authorHan, Weimin
dc.contributor.authorYu, Hengyong
dc.contributor.authorWang, Ge
dc.date.accessioned2017-09-18T09:59:44Z
dc.date.available2017-09-18T09:59:44Z
dc.date.issued2009-11-17
dc.identifier.citationWeimin Han, Hengyong Yu, and Ge Wang, “A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography,” International Journal of Biomedical Imaging, vol. 2009, Article ID 125871, 3 pages, 2009. doi:10.1155/2009/125871
dc.identifier.urihttp://hdl.handle.net/10919/79050
dc.description.abstractRecently, in the compressed sensing framework we found that a two-dimensional interior region-of-interest (ROI) can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant (Yu and Wang, 2009). Here we present a general theorem charactering a minimization property for a piecewise constant function defined on a domain in any dimension. Our major mathematical tool to prove this result is functional analysis without involving the Dirac delta function, which was heuristically used by Yu and Wang (2009).
dc.format.mimetypeapplication/pdf
dc.language.isoenen_US
dc.publisherHindawien_US
dc.rightsCreative Commons Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomographyen_US
dc.typeArticle - Refereed
dc.date.updated2017-09-18T09:59:44Z
dc.description.versionPeer Reviewed
dc.rights.holderCopyright © 2009 Weimin Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.identifier.doihttps://doi.org/10.1155/2009/125871
dc.type.dcmitypeText


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