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Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints

  • Matthias ZislerEmail author
  • Stefania Petra
  • Claudius Schnörr
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

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.

Keywords

Data Term Convex Relaxation Discrete Tomography Integrality Constraint Noiseless Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

419026_1_En_21_MOESM1_ESM.pdf (1.6 mb)
Supplementary material 1 (pdf 1596 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Matthias Zisler
    • 1
    Email author
  • Stefania Petra
    • 1
  • Claudius Schnörr
    • 1
  • Christoph Schnörr
    • 1
  1. 1.Heidelberg UniversityHeidelbergGermany

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