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Conference Paper: Fast image reconstruction algorithms combining half-quadratic regularization and preconditioning

TitleFast image reconstruction algorithms combining half-quadratic regularization and preconditioning
Authors
KeywordsComputers
Computer graphics
Issue Date2001
PublisherIEEE.
Citation
International Conference on Image Processing Proceedings, Thessaloniki, 7-10 October 2001, v. 1, p. 277-280 How to Cite?
AbstractWe focus on image deconvolution and image reconstruction problems where a sought image is recovered from degraded observed data. The solution is defined to be the minimizer of an objective function combining a data-fidelity term and an edge-preserving, convex regularization term. Our objective is to speed up the calculation of the solution in a wide range of situations. To this end, we propose a method applying pertinent preconditioning to an adapted half-quadratic equivalent form of the objective function. The optimal solution is then found using an alternating minimization (AM) scheme. We focus specifically on Huber regularization. We exhibit the possibility of getting very fast calculations while preserving the edges in the solution. Preliminary numerical results are reported to illustrate the effectiveness of our method.
Persistent Identifierhttp://hdl.handle.net/10722/46607
ISSN

 

DC FieldValueLanguage
dc.contributor.authorNikolova, Men_HK
dc.contributor.authorNg, KPen_HK
dc.date.accessioned2007-10-30T06:54:04Z-
dc.date.available2007-10-30T06:54:04Z-
dc.date.issued2001en_HK
dc.identifier.citationInternational Conference on Image Processing Proceedings, Thessaloniki, 7-10 October 2001, v. 1, p. 277-280en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46607-
dc.description.abstractWe focus on image deconvolution and image reconstruction problems where a sought image is recovered from degraded observed data. The solution is defined to be the minimizer of an objective function combining a data-fidelity term and an edge-preserving, convex regularization term. Our objective is to speed up the calculation of the solution in a wide range of situations. To this end, we propose a method applying pertinent preconditioning to an adapted half-quadratic equivalent form of the objective function. The optimal solution is then found using an alternating minimization (AM) scheme. We focus specifically on Huber regularization. We exhibit the possibility of getting very fast calculations while preserving the edges in the solution. Preliminary numerical results are reported to illustrate the effectiveness of our method.en_HK
dc.format.extent360510 bytes-
dc.format.extent4654 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectComputer graphicsen_HK
dc.titleFast image reconstruction algorithms combining half-quadratic regularization and preconditioningen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1522-4880&volume=1&spage=277&epage=280&date=2001&atitle=Fast+image+reconstruction+algorithms+combining+half-quadratic+regularization+and+preconditioningen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICIP.2001.959007en_HK
dc.identifier.hkuros76676-

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