First-order geometric multilevel optimization for discrete tomography

Jan Plier, Fabrizio Savarino, Michal Kocvara, Stefania Petra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publication Scale Space and Variational Methods in Computer Vision
Subtitle of host publication8th International Conference, SSVM 2021, Virtual Event, May 16–20, 2021, Proceedings
EditorsA Elmoataz, J Fadili, Y Queau, J Rabin, L Simon
ISBN (Electronic)9783030755492
ISBN (Print)9783030755485
Publication statusPublished - 30 Apr 2021
EventSSVM 2021: Scale Space and Variational Methods in Computer Vision -
Duration: 16 May 202120 May 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceSSVM 2021: Scale Space and Variational Methods in Computer Vision
Abbreviated titleSSVM 2021

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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