Incomplete oblique projections method for solving regularized least-squares problems in image reconstruction
In this paper we improve on the incomplete oblique projections (IOP) method introduced previously by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method uses IOP onto the set of solutions of the augmented system Ax−r=b, and converges to a w...
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Veröffentlicht in: | International transactions in operational research 2008-07, Vol.15 (4), p.417-438 |
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description | In this paper we improve on the incomplete oblique projections (IOP) method introduced previously by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method uses IOP onto the set of solutions of the augmented system Ax−r=b, and converges to a weighted least‐squares solution of the system Ax=b. In image reconstruction problems, systems are usually inconsistent and very often rank‐deficient because of the underlying discretized model. Here we have considered a regularized least‐squares objective function that can be used in many ways such as incorporating blobs or nearest‐neighbor interactions among adjacent pixels, aiming at smoothing the image. Thus, the oblique incomplete projections algorithm has been modified for solving this regularized model. The theoretical properties of the new algorithm are analyzed and numerical experiments are presented showing that the new approach improves the quality of the reconstructed images. |
doi_str_mv | 10.1111/j.1475-3995.2008.00643.x |
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The theoretical properties of the new algorithm are analyzed and numerical experiments are presented showing that the new approach improves the quality of the reconstructed images.</description><identifier>ISSN: 0969-6016</identifier><identifier>EISSN: 1475-3995</identifier><identifier>DOI: 10.1111/j.1475-3995.2008.00643.x</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Algorithms ; computerized tomography ; Image coding ; image reconstruction ; incomplete projections ; least-squares problems ; minimum norm solution ; Operations research ; regularization ; Studies</subject><ispartof>International transactions in operational research, 2008-07, Vol.15 (4), p.417-438</ispartof><rights>2008 The Authors. 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D.</creatorcontrib><creatorcontrib>Echebest, N. E.</creatorcontrib><creatorcontrib>Guardarucci, M. T.</creatorcontrib><title>Incomplete oblique projections method for solving regularized least-squares problems in image reconstruction</title><title>International transactions in operational research</title><description>In this paper we improve on the incomplete oblique projections (IOP) method introduced previously by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method uses IOP onto the set of solutions of the augmented system Ax−r=b, and converges to a weighted least‐squares solution of the system Ax=b. In image reconstruction problems, systems are usually inconsistent and very often rank‐deficient because of the underlying discretized model. Here we have considered a regularized least‐squares objective function that can be used in many ways such as incorporating blobs or nearest‐neighbor interactions among adjacent pixels, aiming at smoothing the image. Thus, the oblique incomplete projections algorithm has been modified for solving this regularized model. The theoretical properties of the new algorithm are analyzed and numerical experiments are presented showing that the new approach improves the quality of the reconstructed images.</description><subject>Algorithms</subject><subject>computerized tomography</subject><subject>Image coding</subject><subject>image reconstruction</subject><subject>incomplete projections</subject><subject>least-squares problems</subject><subject>minimum norm solution</subject><subject>Operations research</subject><subject>regularization</subject><subject>Studies</subject><issn>0969-6016</issn><issn>1475-3995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqNkMtOwzAQRS0EEuXxDxb7BDt24njBAiooRRVIPMTSSpxpSXDjYifQ8vU4LWLNbGYk33M9cxHClMQ01HkTUy7SiEmZxgkheUxIxlm83kOjv4d9NCIyk1FGaHaIjrxvCCE0pWKEzLTVdrky0AG2pak_esArZxvQXW1bj5fQvdkKz63D3prPul1gB4veFK7-hgobKHwX-Y--cOAHsDSw9Lhucb0sFhC0Orh0rt_anaCDeWE8nP72Y_Ryc_08vo1mD5Pp-HIWaSY4ixiQpCpLQaqU5lmpgaVSlyTRQoLgPJFc5CITVDBNc815mEBrCTrJJU_Kgh2js51vWCgc5DvV2N614UuVDIdLIWUQ5TuRdtZ7B3O1cmFpt1GUqCFa1aghQTUkOHC52kar1gG92KFftYHNvzk1fX54DFPgox1f-w7Wf3zh3lUmWEBf7ydKjp8mguZ36or9AKockL4</recordid><startdate>200807</startdate><enddate>200807</enddate><creator>Scolnik, H. 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T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Incomplete oblique projections method for solving regularized least-squares problems in image reconstruction</atitle><jtitle>International transactions in operational research</jtitle><date>2008-07</date><risdate>2008</risdate><volume>15</volume><issue>4</issue><spage>417</spage><epage>438</epage><pages>417-438</pages><issn>0969-6016</issn><eissn>1475-3995</eissn><abstract>In this paper we improve on the incomplete oblique projections (IOP) method introduced previously by the authors for solving inconsistent linear systems, when applied to image reconstruction problems. That method uses IOP onto the set of solutions of the augmented system Ax−r=b, and converges to a weighted least‐squares solution of the system Ax=b. In image reconstruction problems, systems are usually inconsistent and very often rank‐deficient because of the underlying discretized model. Here we have considered a regularized least‐squares objective function that can be used in many ways such as incorporating blobs or nearest‐neighbor interactions among adjacent pixels, aiming at smoothing the image. Thus, the oblique incomplete projections algorithm has been modified for solving this regularized model. The theoretical properties of the new algorithm are analyzed and numerical experiments are presented showing that the new approach improves the quality of the reconstructed images.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1475-3995.2008.00643.x</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms computerized tomography Image coding image reconstruction incomplete projections least-squares problems minimum norm solution Operations research regularization Studies |
title | Incomplete oblique projections method for solving regularized least-squares problems in image reconstruction |
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