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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| Autori principali: | , , |
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| Natura: | Chapter/Article Conference Paper |
| Lingua: | inglese |
| Pubblicazione: |
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 |
| Soggetti: | |
| Accesso online: | 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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| Note sull'autore: | Jan Kuske, Paul Swoboda, Stefania Petra |
| Riassunto: | 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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| Descrizione del documento: | Gesehen am 14.03.2018 |
| Descrizione fisica: | Online Resource |
| ISBN: | 9783319587714 |
| DOI: | 10.1007/978-3-319-58771-4_19 |