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Conference Paper (international conference)

First-order geometric multilevel optimization for discrete tomography

Plier J., Savarino F., Kočvara Michal, Petra S.

: Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021, p. 191-203

: International Conference on Scale Space and Variational Methods in Computer Vision : SSVM 2021 /8./, (Virtual Event, CH, 20210516)

: discrete tomography, multilevel optimization, n-orthotope

: 10.1007/978-3-030-75549-2_16

: http://library.utia.cas.cz/separaty/2021/MTR/kocvara-0542259.pdf

(eng): 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.

: BA

: 10102

07.01.2019 - 08:39