Non-binary discrete tomography by continuous non-convex optimization
We study an energy formulation for non-binary discrete tomography and introduce a non-convex coupling term in order to combine discrete constraints with a continuous reconstruction method based on total variation regularization. The optimization is carried out by a generalized forward-backward split...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
04 May 2016
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| In: |
IEEE transactions on computational imaging
Year: 2016, Jahrgang: 2, Heft: 3, Pages: 335-347 |
| ISSN: | 2333-9403 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org./10.1109/TCI.2016.2563321 Verlag, Volltext: http://ieeexplore.ieee.org/document/7464896/ Verlag, Volltext: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7464896 
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| Verfasserangaben: | M. Zisler, J.H. Kappes, C. Schnörr, S. Petra, C. Schnörr |
| Zusammenfassung: | We study an energy formulation for non-binary discrete tomography and introduce a non-convex coupling term in order to combine discrete constraints with a continuous reconstruction method based on total variation regularization. The optimization is carried out by a generalized forward-backward splitting algorithm for non-convex functions, which exploits the problem structure and is guaranteed to globally converge to a local optimum. A detailed numerical evaluation on standard test-datasets demonstrates that the proposed algorithm returns more accurate reconstructions from a few number of projection angles than competing methods. |
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| Beschreibung: | Gesehen am 15.03.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 2333-9403 |