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First-order geometric multilevel optimization for discrete tomography
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SYSNO ASEP 0542259 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title First-order geometric multilevel optimization for discrete tomography Author(s) Plier, J. (DE)
Savarino, F. (DE)
Kočvara, Michal (UTIA-B) RID, ORCID
Petra, S. (DE)Number of authors 4 Source Title Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021. - Cham : Springer, 2021 - ISBN 978-3-030-75549-2 Pages s. 191-203 Number of pages 13 s. Publication form Print - P Action International Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2021 /8./ Event date 16.05.2021 - 20.05.2021 VEvent location Virtual Event Country CH - Switzerland Event type WRD Language eng - English Country CH - Switzerland Keywords discrete tomography ; multilevel optimization ; n-orthotope Subject RIV BA - General Mathematics OECD category Applied mathematics Institutional support UTIA-B - RVO:67985556 DOI 10.1007/978-3-030-75549-2_16 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
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