Image Reconstruction by Multilabel Propagation

  • Matthias ZislerEmail author
  • Freddie Åström
  • Stefania Petra
  • Christoph Schnörr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)


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.


Convex Relaxation Proximal Mapping Discrete Probability Distribution Fixed Point Iteration Discrete Tomography 
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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Matthias Zisler
    • 1
    Email author
  • Freddie Åström
    • 1
  • Stefania Petra
    • 2
  • Christoph Schnörr
    • 1
  1. 1.Image and Pattern Analysis GroupHeidelberg UniversityHeidelbergGermany
  2. 2.Mathematical Imaging GroupHeidelberg UniversityHeidelbergGermany

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