---
res:
  bibo_abstract:
  - We present a novel convex relaxation and a corresponding inference algorithm for
    the non-binary discrete tomography problem, that is, reconstructing discrete-valued
    images from few linear measurements. In contrast to state of the art approaches
    that split the problem into a continuous reconstruction problem for the linear
    measurement constraints and a discrete labeling problem to enforce discrete-valued
    reconstructions, we propose a joint formulation that addresses both problems simultaneously,
    resulting in a tighter convex relaxation. For this purpose a constrained graphical
    model is set up and evaluated using a novel relaxation optimized by dual decomposition.
    We evaluate our approach experimentally and show superior solutions both mathematically
    (tighter relaxation) and experimentally in comparison to previously proposed relaxations.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Jan
      foaf_name: Kuske, Jan
      foaf_surname: Kuske
  - foaf_Person:
      foaf_givenName: Paul
      foaf_name: Swoboda, Paul
      foaf_surname: Swoboda
      foaf_workInfoHomepage: http://www.librecat.org/personId=446560C6-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Stefanie
      foaf_name: Petra, Stefanie
      foaf_surname: Petra
  bibo_doi: 10.1007/978-3-319-58771-4_19
  bibo_volume: 10302
  dct_date: 2017^xs_gYear
  dct_identifier:
  - UT:000432210900019
  dct_isPartOf:
  - http://id.crossref.org/issn/978-331958770-7
  dct_language: eng
  dct_publisher: Springer@
  dct_title: A novel convex relaxation for non binary discrete tomography@
...