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Bredies, K.* ; Holler, M.* ; Storath, M.* ; Weinmann, A.

Total generalized variation for manifold-valued data.

SIAM J. Imaging Sci. 11, 1785-1848 (2018)
Postprint DOI
Open Access Green
In this paper we introduce the notion of second-order total generalized variation (TGV) regularization for manifold-valued data in a discrete setting. We provide an axiomatic approach to formalize reasonable generalizations of TGV to the manifold setting and present two possible concrete instances that fulfill the proposed axioms. We provide well-posedness results and present algorithms for a numerical realization of these generalizations to the manifold setup. Further, we provide experimental results for synthetic and real data to further underpin the proposed generalization numerically and show its potential for applications with manifold-valued data.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Denoising ; Higher Order Regularization ; Manifold-valued Data ; Total Generalized Variation; Total Variation Regularization; Proximal Point Algorithm; Extrinsic Sample Means; Tgv-based Framework; Riemannian-manifolds; Image Decompression; Bounded Variation; Corpus-callosum; Diffusion; Spaces
Sprache
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 1936-4954
Zeitschrift SIAM Journal on Imaging Sciences
Quellenangaben Band: 11, Heft: 3, Seiten: 1785-1848 Artikelnummer: , Supplement: ,
Verlag SIAM
Verlagsort Philadelphia, Pa.
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
DOI 10.1137/17M1147597
WOS ID WOS:000446062900001
Scopus ID 85055767386
Erfassungsdatum 2018-10-19
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