@inproceedings{646,
  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.},
  author       = {Kuske, Jan and Swoboda, Paul and Petra, Stefanie},
  editor       = {Lauze, François and Dong, Yiqiu and Bjorholm Dahl, Anders},
  isbn         = {978-331958770-7},
  location     = {Kolding, Denmark},
  pages        = {235 -- 246},
  publisher    = {Springer},
  title        = {{A novel convex relaxation for non binary discrete tomography}},
  doi          = {10.1007/978-3-319-58771-4_19},
  volume       = {10302},
  year         = {2017},
}