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Weinmann, A. ; Storath, M.*

Iterative Potts and Blake-Zisserman minimization for the recovery of functions with discontinuities from indirect measurements.

Proc. R. Soc. London A 471:20140638 (2015)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Signals with discontinuities appear in many problems in the applied sciences ranging from mechanics, electrical engineering to biology and medicine. The concrete data acquired are typically discrete, indirect and noisy measurements of some quantities describing the signal under consideration. The task is to restore the signal and, in particular, the discontinuities. In this respect, classical methods perform rather poor, whereas non-convex non-smooth variational methods seem to be the correct choice. Examples are methods based on Mumford-Shah and piecewise constant Mumford-Shah functionals and discretized versions which are known as Blake- Zisserman and Potts functionals. Owing to their non-convexity, minimization of such functionals is challenging. In this paper, we propose a new iterative minimization strategy for Blake-Zisserman as well as Potts functionals and a related jump-sparsity problem dealing with indirect, noisy measurements. We provide a convergence analysis and underpin our findings with numerical experiments.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Blake-zisserman Functionals ; Convergence Analysis ; Inverse Problems ; Piecewise Constant Signals ; Potts Functionals ; Signals With Discontinuities; Bacterial Flagellar Motor; Least-squares Estimators; Sparsity Constraint; Generalized Methods; Noise Removal; Approximations; Complexity; Regularization; Convergence; Algorithms
ISSN (print) / ISBN 1364-5021
e-ISSN 1471-2946
Zeitschrift Proceedings of the Royal Society of London
Quellenangaben Band: 471, Heft: 2176, Seiten: , Artikelnummer: 20140638 Supplement: ,
Verlag Royal Society of London
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)