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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| Main Authors: | , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
27 August 2016
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| In: |
Pattern Recognition
Year: 2016, Pages: 261-272 |
| DOI: | 10.1007/978-3-319-45886-1_21 |
| Subjects: | |
| Online Access: | 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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| Author Notes: | Matthias Zisler, Stefania Petra, Claudius Schnörr, Christoph Schnörr |
| Summary: | 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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| Item Description: | Gesehen am 13.03.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9783319458861 |
| DOI: | 10.1007/978-3-319-45886-1_21 |