Non-binary discrete tomography by continuous non-convex optimization

• Verfasst von: Zisler, Matthias [VerfasserIn]
• Verfasst von: Kappes, Jörg Hendrik [VerfasserIn]
• Verfasst von: Schnörr, Claudius [VerfasserIn]
• Verfasst von: Petra, Stefania [VerfasserIn]
• Verfasst von: Schnörr, Christoph [VerfasserIn]
• Titel: Non-binary discrete tomography by continuous non-convex optimization
• Verf.angabe: M. Zisler, J.H. Kappes, C. Schnörr, S. Petra, C. Schnörr
• Umfang: 13 S.
• Fussnoten: Gesehen am 15.03.2018
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics Engineers: IEEE transactions on computational imaging
• Jahr Quelle: 2016
• Band/Heft Quelle: 2(2016), 3, S. 335-347
• ISSN Quelle: 2333-9403
• DOI: doi:10.1109/TCI.2016.2563321
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1571090762

Abstract

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.