A novel convex relaxation for non-binary discrete tomography
We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction...
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| 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: 235-246 |
| DOI: | 10.1007/978-3-319-58771-4_19 |
| Schlagworte: | |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-58771-4_19 Verlag, Volltext: https://link.springer.com/chapter/10.1007/978-3-319-58771-4_19 
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| Verfasserangaben: | Jan Kuske, Paul Swoboda, Stefania Petra |
MARC
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| 520 | |a We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations. | ||
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