Image reconstruction by multilabel propagation
This work presents a non-convex variational approach to joint image reconstruction and labeling. Our regularization strategy, based on the KL-divergence, takes into account the smooth geometry on the space of discrete probability distributions. The proposed objective function is efficiently minimize...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
18 May 2017
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| In: |
Scale Space and Variational Methods in Computer Vision
Year: 2017, Pages: 247-259 |
| DOI: | 10.1007/978-3-319-58771-4_20 |
| Schlagworte: | |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-58771-4_20 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-58771-4_20 
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| Verfasserangaben: | Matthias Zisler, Freddie Åström, Stefania Petra, Christoph Schnörr |
MARC
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| 520 | |a This work presents a non-convex variational approach to joint image reconstruction and labeling. Our regularization strategy, based on the KL-divergence, takes into account the smooth geometry on the space of discrete probability distributions. The proposed objective function is efficiently minimized via DC programming which amounts to solving a sequence of convex programs, with guaranteed convergence to a critical point. Each convex program is solved by a generalized primal dual algorithm. This entails the evaluation of a proximal mapping, evaluated efficiently by a fixed point iteration. We illustrate our approach on few key scenarios in discrete tomography and image deblurring. | ||
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