First-order geometric multilevel optimization for discrete tomography

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00542259" target="_blank" >RIV/67985556:_____/21:00542259 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-75549-2_16" target="_blank" >http://dx.doi.org/10.1007/978-3-030-75549-2_16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-75549-2_16" target="_blank" >10.1007/978-3-030-75549-2_16</a>

Result language
angličtina
Original language name
First-order geometric multilevel optimization for discrete tomography
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Scale Space and Variational Methods in Computer Vision: 8th International Conference, SSVM 2021
ISBN
978-3-030-75549-2
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
191-203
Publisher name
Springer
Place of publication
Cham
Event location
Virtual Event
Event date
May 16, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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