DC Field | Value | Language |
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dc.contributor.author | Ye, Jong Chul | ko |
dc.date.accessioned | 2013-03-07T19:16:24Z | - |
dc.date.available | 2013-03-07T19:16:24Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2007-10 | - |
dc.identifier.citation | IEEE SIGNAL PROCESSING LETTERS, v.14, pp.750 - 753 | - |
dc.identifier.issn | 1070-9908 | - |
dc.identifier.uri | http://hdl.handle.net/10203/91040 | - |
dc.description.abstract | Recent theory of compressed sensing informs us that near-exact recovery of an unknown sparse signal is possible from a very limited number of Fourier samples by solving a convex L-1 optimization problem. The main contribution of the present letter is a compressed sensing-based novel nonparametric shape estimation framework and a computational algorithm for binary star shape objects, whose radius functions belong to the space of bounded-variation functions. Specifically, in contrast with standard compressed sensing, the present approach involves directly reconstructing the-shape boundary under sparsity constraint. This is done by converting the standard pixel-based reconstruction approach into estimation of a nonparametric shape boundary on a wavelet basis. This results in an L-1 minimization under a nonlinear constraint, which makes the optimization problem nonconvex. We solve the problem by successive linearization and application of one-dimensional L-1 minimization, which significantly reduces the number of sampling requirements as well as the computational burden. Fourier imaging simulation results demonstrate that high quality reconstruction can be quickly obtained from a very limited number of samples. Furthermore, the algorithm outperforms the standard compressed sensing reconstruction approach using the total variation norm. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Compressed sensing shape estimation of star-shaped objects in Fourier imaging | - |
dc.type | Article | - |
dc.identifier.wosid | 000249941900027 | - |
dc.identifier.scopusid | 2-s2.0-34548781325 | - |
dc.type.rims | ART | - |
dc.citation.volume | 14 | - |
dc.citation.beginningpage | 750 | - |
dc.citation.endingpage | 753 | - |
dc.citation.publicationname | IEEE SIGNAL PROCESSING LETTERS | - |
dc.identifier.doi | 10.1109/LSP.2007.898342 | - |
dc.contributor.localauthor | Ye, Jong Chul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | compressed sensing | - |
dc.subject.keywordAuthor | Fourier imaging | - |
dc.subject.keywordAuthor | L-1 | - |
dc.subject.keywordAuthor | minimization | - |
dc.subject.keywordAuthor | nonparametric shape estimation | - |
dc.subject.keywordPlus | GLOBAL CONFIDENCE-REGIONS | - |
dc.subject.keywordPlus | INVERSE PROBLEMS | - |
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