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dc.contributor.authorZhang, Bo
dc.contributor.authorMakram-Ebeid, Sherif
dc.contributor.authorPrevost, Raphaël
dc.contributor.authorPizaine, Guillaume
dc.date.accessioned2015-04-14T17:38:02Z
dc.date.available2015-04-14T17:38:02Z
dc.date.issued2014
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14941
dc.language.isoenen
dc.subjectRegularized weighted least-squaresen
dc.subjectPreconditioned conjugate gradienten
dc.subjectPreconditioningen
dc.subjectCondition numberen
dc.subject.ddc519en
dc.titleFast solver for some computational imaging problems: A regularized weighted least-squares approachen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn this paper we propose to solve a range of computational imaging problems under a unified perspective of a regularized weighted least-squares (RWLS) framework. These problems include data smoothing and completion, edge-preserving filtering, gradient-vector flow estimation, and image registration. Although originally very different, they are special cases of the RWLS model using different data weightings and regularization penalties. Numerically, we propose a preconditioned conjugate gradient scheme which is particularly efficient in solving RWLS problems. We provide a detailed analysis of the system conditioning justifying our choice of the preconditioner that improves the convergence. This numerical solver, which is simple, scalable and parallelizable, is found to outperform most of the existing schemes for these imaging problems in terms of convergence rate.en
dc.relation.isversionofjnlnameDigital Signal Processing
dc.relation.isversionofjnlvol27en
dc.relation.isversionofjnldate2014
dc.relation.isversionofjnlpages107-118en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.dsp.2014.01.007en
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01097421en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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