A Primal-Dual Approach for a Total Variation Wasserstein Flow

Handle URI:
http://hdl.handle.net/10754/597380
Title:
A Primal-Dual Approach for a Total Variation Wasserstein Flow
Authors:
Benning, Martin; Calatroni, Luca; Düring, Bertram; Schönlieb, Carola-Bibiane
Abstract:
We consider a nonlinear fourth-order diffusion equation that arises in denoising of image densities. We propose an implicit time-stepping scheme that employs a primal-dual method for computing the subgradient of the total variation seminorm. The constraint on the dual variable is relaxed by adding a penalty term, depending on a parameter that determines the weight of the penalisation. The paper is furnished with some numerical examples showing the denoising properties of the model considered. © 2013 Springer-Verlag.
Citation:
Benning M, Calatroni L, Düring B, Schönlieb C-B (2013) A Primal-Dual Approach for a Total Variation Wasserstein Flow. Geometric Science of Information: 413–421. Available: http://dx.doi.org/10.1007/978-3-642-40020-9_45.
Publisher:
Springer Science + Business Media
Journal:
Lecture Notes in Computer Science
KAUST Grant Number:
KUK-I1-007-43
Issue Date:
2013
DOI:
10.1007/978-3-642-40020-9_45
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Sponsors:
Carola-Bibiane Sch¨onlieb acknowledges financial supportprovided by the Cambridge Centre for Analysis (CCA), the Royal Society InternationalExchanges Award IE110314 for the project High-order CompressedSensing for Medical Imaging, the EPSRC first grant Nr. EP/J009539/1 Sparse& Higher-order Image Restoration, and the EPSRC / Isaac Newton Trust SmallGrant on Non-smooth geometric reconstruction for high resolution MRI imagingof fluid transport in bed reactors. Further, this publication is based on worksupported by Award No. KUK-I1-007-43, made by King Abdullah University ofScience and Technology (KAUST).
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorBenning, Martinen
dc.contributor.authorCalatroni, Lucaen
dc.contributor.authorDüring, Bertramen
dc.contributor.authorSchönlieb, Carola-Bibianeen
dc.date.accessioned2016-02-25T12:32:01Zen
dc.date.available2016-02-25T12:32:01Zen
dc.date.issued2013en
dc.identifier.citationBenning M, Calatroni L, Düring B, Schönlieb C-B (2013) A Primal-Dual Approach for a Total Variation Wasserstein Flow. Geometric Science of Information: 413–421. Available: http://dx.doi.org/10.1007/978-3-642-40020-9_45.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-642-40020-9_45en
dc.identifier.urihttp://hdl.handle.net/10754/597380en
dc.description.abstractWe consider a nonlinear fourth-order diffusion equation that arises in denoising of image densities. We propose an implicit time-stepping scheme that employs a primal-dual method for computing the subgradient of the total variation seminorm. The constraint on the dual variable is relaxed by adding a penalty term, depending on a parameter that determines the weight of the penalisation. The paper is furnished with some numerical examples showing the denoising properties of the model considered. © 2013 Springer-Verlag.en
dc.description.sponsorshipCarola-Bibiane Sch¨onlieb acknowledges financial supportprovided by the Cambridge Centre for Analysis (CCA), the Royal Society InternationalExchanges Award IE110314 for the project High-order CompressedSensing for Medical Imaging, the EPSRC first grant Nr. EP/J009539/1 Sparse& Higher-order Image Restoration, and the EPSRC / Isaac Newton Trust SmallGrant on Non-smooth geometric reconstruction for high resolution MRI imagingof fluid transport in bed reactors. Further, this publication is based on worksupported by Award No. KUK-I1-007-43, made by King Abdullah University ofScience and Technology (KAUST).en
dc.publisherSpringer Science + Business Mediaen
dc.titleA Primal-Dual Approach for a Total Variation Wasserstein Flowen
dc.typeBook Chapteren
dc.identifier.journalLecture Notes in Computer Scienceen
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdomen
dc.contributor.institutionUniversity of Sussex, Sussex, United Kingdomen
kaust.grant.numberKUK-I1-007-43en
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