A comparison of sparse Bayesian regularization methods on computed tomography reconstruction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F18%3A43951832" target="_blank" >RIV/49777513:23220/18:43951832 - isvavai.cz</a>
Result on the web
<a href="http://iopscience.iop.org/article/10.1088/1742-6596/1047/1/012013/meta" target="_blank" >http://iopscience.iop.org/article/10.1088/1742-6596/1047/1/012013/meta</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/1047/1/012013" target="_blank" >10.1088/1742-6596/1047/1/012013</a>
Alternative languages
Result language
angličtina
Original language name
A comparison of sparse Bayesian regularization methods on computed tomography reconstruction
Original language description
Design of regularization term is an important part of solution of an ill-posed linear inverse problem. Another important issue is selection of tuning parameters of the regularization term. We address this problem using Bayesian approach which treats tuning parameters as unknowns and estimates them from the data. Specifically, we study a regularization model known as Automatic Relevance Determination (ARD) and several methods of its solution. The first approach is the conventional Variational Bayes method using the symmetrical factorization of the posterior of the vector of unknowns and the vector of tuning parameters. The second approach is based on the idea of marginalization over the vector of unknowns or the vector of tuning parameters, while the complementary vector is estimated using maximum likelihood. The resulting algorithm is thus an optimization task with non-convex objective function, which is solved using standard gradient methods. The proposed algorithms are tested on real tomographic X-ray data and the comparison with conventional regularization techniques (Tikhonov and Lasso) is performed. The algorithm using marginalization over the tuning parameter is found to be closest to the ground truth with acceptable computational cost. MATLAB®implementation of the reconstruction algorithms is freely available for download.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/LO1607" target="_blank" >LO1607: RICE - New technologies and concepts for smart industrial system</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Journal of Physics: Conference Series; 1047
ISBN
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ISSN
1742-6588
e-ISSN
1742-6596
Number of pages
17
Pages from-to
1-17
Publisher name
IOP Publishing Ltd.
Place of publication
Bristol
Event location
Waterloo, Canada
Event date
May 23, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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