Image reconstruction by multilabel propagation

• Verfasst von: Zisler, Matthias [VerfasserIn]
• Verfasst von: Åström, Freddie [VerfasserIn]
• Verfasst von: Petra, Stefania [VerfasserIn]
• Verfasst von: Schnörr, Christoph [VerfasserIn]
• Titel: Image reconstruction by multilabel propagation
• Verf.angabe: Matthias Zisler, Freddie Åström, Stefania Petra, Christoph Schnörr
• Umfang: 13 S.
• Fussnoten: Gesehen am 14.03.2018
• Titel Quelle: Enthalten in: Scale Space and Variational Methods in Computer Vision
• Jahr Quelle: 2017
• Band/Heft Quelle: (2017), S. 247-259
• ISBN Quelle: 978-3-319-58771-4
• DOI: doi:10.1007/978-3-319-58771-4_20
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1571047999

Abstract

This work presents a non-convex variational approach to joint image reconstruction and labeling. Our regularization strategy, based on the KL-divergence, takes into account the smooth geometry on the space of discrete probability distributions. The proposed objective function is efficiently minimized via DC programming which amounts to solving a sequence of convex programs, with guaranteed convergence to a critical point. Each convex program is solved by a generalized primal dual algorithm. This entails the evaluation of a proximal mapping, evaluated efficiently by a fixed point iteration. We illustrate our approach on few key scenarios in discrete tomography and image deblurring.