0542259 20240103225755.720210511235959.9 10.1007/978-3-030-75549-2_16 13 s. P cav_un_epca*0542258978-3-030-75549-2191-203ChamSpringer2021 discrete tomography multilevel optimization n-orthotope cav_un_auth*0408933 Plier J. DE cav_un_auth*0408934 Savarino F. DE cav_un_auth*0101131 Kočvara Michal UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0408935 Petra S. DE http://library.utia.cas.cz/separaty/2021/MTR/kocvara-0542259.pdf Discrete tomography (DT) naturally leads to a hierarchy of models of varying discretization levels. We employ multilevel optimization (MLO) to take advantage of this hierarchy: while working at the fine level we compute the search direction based on a coarse model. Importing concepts from information geometry to the n-orthotope, we propose a smoothing operator that only uses first-order information and incorporates constraints smoothly. We show that the proposed algorithm is well suited to the ill-posed reconstruction problem in DT, compare it to a recent MLO method that nonsmoothly incorporates box constraints and demonstrate its efficiency on several large-scale examples. cav_un_auth*0410997 International Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2021 /8./ 20210516 20210520 Virtual Event CH BA 10000 10100 10102 2022 4 PR RVO:67985556 http://hdl.handle.net/11104/0320772 S 2021 SCOPUS PUBMED WOS cav_un_epca*0542258 Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021 978-3-030-75549-2 191 203 Cham Springer 2021 Lecture Notes in Computer Science 12679