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dc.contributor.authorVan Chung, C
dc.contributor.authorDe los Reyes, JC
dc.contributor.authorSchönlieb, CB
dc.date.accessioned2019-05-13T12:45:07Z
dc.date.available2019-05-13T12:45:07Z
dc.date.issued2017-06-21
dc.identifier.issn0266-5611
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/292704
dc.description.abstractWe consider a bilevel optimization approach in function space for the choice of spatially dependent regularization parameters in TV image denoising models. First- and second-order optimality conditions for the bilevel problem are studied when the spatially-dependent parameter belongs to the Sobolev space H1(Ω). A combined Schwarz domain decomposition-semismooth Newton method is proposed for the solution of the full optimality system and local superlinear convergence of the semismooth Newton method is verified. Exhaustive numerical computations are finally carried out to show the suitability of the approach.
dc.publisherIOP Science
dc.titleLearning optimal spatially-dependent regularization parameters in total variation image denoising
dc.typeArticle
prism.issueIdentifier7
prism.publicationDate2017
prism.publicationNameInverse Problems
prism.volume33
dc.identifier.doi10.17863/CAM.39857
dcterms.dateAccepted2016-11-30
rioxxterms.versionofrecord10.1088/1361-6420/33/7/074005
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-06-21
dc.identifier.eissn1361-6420
dc.publisher.urlhttps://iopscience.iop.org/article/10.1088/1361-6420/33/7/074005/meta
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N014588/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/M00483X/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/J009539/1)
pubs.funder-project-idAlan Turing Institute (unknown)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (691070)
cam.issuedOnline2017-06-21
dc.identifier.urlhttps://iopscience.iop.org/article/10.1088/1361-6420/33/7/074005/meta


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