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Multiresolution Parameter Choice Method for Total Variation Regularized Tomography
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).
Vise andre og tillknytning
2016 (engelsk)Inngår i: SIAM Journal on Imaging Sciences, ISSN 1936-4954, E-ISSN 1936-4954, Vol. 9, nr 3, s. 938-974Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A computational method is introduced for choosing the regularization parameter for total variation (TV) regularization. A partial understanding of the properties of the method is provided by rigorously proving that the TV norms of the reconstructions converge with any choice of regularization parameter. The computational approach is based on computing reconstructions at a few different resolutions and various values of regularization parameter. The chosen parameter is the smallest one resulting in approximately discretization-invariant TV norms of the reconstructions. The method is tested with simulated and experimental X-ray tomography data and compared to the S-curve method. The results are comparable to those of the S-curve method. However, the S-curve method needs quantitative a priori information about the expected sparsity (TV norm) of the unknown, while the proposed method does not require such input parameters.

sted, utgiver, år, opplag, sider
SIAM PUBLICATIONS , 2016. Vol. 9, nr 3, s. 938-974
Emneord [en]
total variation regularization, parameter choice, tomography, inverse problem, regularization
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-196490DOI: 10.1137/15M1034076ISI: 000385277200003Scopus ID: 2-s2.0-84989321781OAI: oai:DiVA.org:kth-196490DiVA, id: diva2:1049294
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QC 20161124

Tilgjengelig fra: 2016-11-24 Laget: 2016-11-14 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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