Discrete tomography by continuous multilabeling subject to projection constraints
We present a non-convex variational approach to non-binary discrete tomography which combines non-local projection constraints with a continuous convex relaxation of the multilabeling problem. Minimizing this non-convex energy is achieved by a fixed point iteration which amounts to solving a sequenc...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
27 August 2016
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| In: |
Pattern Recognition
Year: 2016, Pages: 261-272 |
| DOI: | 10.1007/978-3-319-45886-1_21 |
| Schlagworte: | |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-45886-1_21 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-45886-1_21 
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| Verfasserangaben: | Matthias Zisler, Stefania Petra, Claudius Schnörr, Christoph Schnörr |
MARC
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| 520 | |a We present a non-convex variational approach to non-binary discrete tomography which combines non-local projection constraints with a continuous convex relaxation of the multilabeling problem. Minimizing this non-convex energy is achieved by a fixed point iteration which amounts to solving a sequence of convex problems, with guaranteed convergence to a critical point. A competitive numerical evaluation using standard test-datasets demonstrates a significantly improved reconstruction quality for noisy measurements from a small number of projections. | ||
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