A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography

dc.contributor.authorHan, Weiminen
dc.contributor.authorYu, Hengyongen
dc.contributor.authorWang, Geen
dc.contributor.departmentSchool of Biomedical Engineering and Sciencesen
dc.date.accessioned2017-09-18T09:59:44Zen
dc.date.available2017-09-18T09:59:44Zen
dc.date.issued2009-11-17en
dc.date.updated2017-09-18T09:59:44Zen
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).en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
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:10.1155/2009/125871en
dc.identifier.doihttps://doi.org/10.1155/2009/125871en
dc.identifier.urihttp://hdl.handle.net/10919/79050en
dc.language.isoenen
dc.publisherHindawien
dc.rightsCreative Commons Attribution 4.0 Internationalen
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.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomographyen
dc.title.serialInternational Journal of Biomedical Imagingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
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