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Scale-Covariant and Scale-Invariant Gaussian Derivative Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

First-Order Geometric Multilevel Optimization for Discrete Tomography

Authors : Jan Plier, Fabrizio Savarino, Michal Kočvara, Stefania Petra

Published in: Scale Space and Variational Methods in Computer Vision

Publisher: Springer International Publishing

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Abstract

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.

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Footnotes
1
Our model does not require this assumption.
 
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Metadata
Title
First-Order Geometric Multilevel Optimization for Discrete Tomography
Authors
Jan Plier
Fabrizio Savarino
Michal Kočvara
Stefania Petra
Copyright Year
2021
Book
Scale Space and Variational Methods in Computer Vision

Print ISBN: 978-3-030-75548-5

Electronic ISBN: 978-3-030-75549-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75549-2_16

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