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dc.contributor.authorPapafitsoros, Konstantinos
dc.contributor.authorValkonen, Tuomo
dc.date.accessioned2016-02-25T12:43:12Z
dc.date.available2016-02-25T12:43:12Z
dc.date.issued2015-04-28
dc.identifier.citationPapafitsoros K, Valkonen T (2015) Asymptotic Behaviour of Total Generalised Variation. Scale Space and Variational Methods in Computer Vision: 702–714. Available: http://dx.doi.org/10.1007/978-3-319-18461-6_56.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.doi10.1007/978-3-319-18461-6_56
dc.identifier.urihttp://hdl.handle.net/10754/597621
dc.description.abstract© Springer International Publishing Switzerland 2015. The recently introduced second order total generalised variation functional TGV2 β,α has been a successful regulariser for image processing purposes. Its definition involves two positive parameters α and β whose values determine the amount and the quality of the regularisation. In this paper we report on the behaviour of TGV2 β,α in the cases where the parameters α, β as well as their ratio β/α becomes very large or very small. Among others, we prove that for sufficiently symmetric two dimensional data and large ratio β/α, TGV2 β,α regularisation coincides with total variation (TV) regularization
dc.description.sponsorshipThis work is supported by the King Abdullah University for Science and Technology (KAUST) Award No. KUK-I1-007-43. The first author acknowledges further support by the Cambridge Centre for Analysis (CCA) and the Engineering and Physical Sciences Research Council (EPSRC). The second author acknowledges further support from EPSRC grant EP/M00483X/1 “Efficient computational tools for inverse imaging problems”.
dc.publisherSpringer Nature
dc.subjectAsymptotic behaviour of regularisers
dc.subjectRegularisation parameters
dc.subjectTotal generalised variation
dc.subjectTotal variation
dc.titleAsymptotic Behaviour of Total Generalised Variation
dc.typeBook Chapter
dc.identifier.journalScale Space and Variational Methods in Computer Vision
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdom
kaust.grant.numberKUK-I1-007-43
dc.date.published-online2015-04-28
dc.date.published-print2015


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