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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
An algorithmic framework for Mumford-Shah regularization of inverse problems in imaging.
Inverse Probl. 31:115011 (2015)
The Mumford–Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this paper, we propose a new efficient algorithmic framework for Mumford–Shah regularization of inverse problems in imaging. It is based on a splitting into specific subproblems that can be solved exactly. We derive fast solvers for the subproblems which are key for an efficient overall algorithm. Our method neither requires a priori knowledge of the gray or color levels nor of the shape of the discontinuity set. We demonstrate the wide applicability of the method for different modalities. In particular, we consider the reconstruction from Radon data, inpainting, and deconvolution. Our method can be easily adapted to many further imaging setups. The relevant condition is that the proximal mapping of the data fidelity can be evaluated a within reasonable time. In other words, it can be used whenever classical Tikhonov regularization is possible.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Admm ; Computed Tomography ; Deconvolution ; Dynamic Programming ; Image Reconstruction ; Inverse Problem ; Mumford-shah Functional
ISSN (print) / ISBN
0266-5611
e-ISSN
1361-6420
Zeitschrift
Inverse Problems
Quellenangaben
Band: 31,
Heft: 11,
Artikelnummer: 115011
Verlag
Institute of Physics Publishing (IOP)
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)