--- 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_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@ ...