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Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors

We present an approach for variational regularization of inverse and imaging problems for recovering functions with values in a set of vectors. We introduce regularization functionals, which are derivative-free double integrals of such functions. These regularization functionals are motivated from d...

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Detalles Bibliográficos
Autores principales: Ciak, René, Melching, Melanie, Scherzer, Otmar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6647495/
https://www.ncbi.nlm.nih.gov/pubmed/31396002
http://dx.doi.org/10.1007/s10851-018-00869-6
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author Ciak, René
Melching, Melanie
Scherzer, Otmar
author_facet Ciak, René
Melching, Melanie
Scherzer, Otmar
author_sort Ciak, René
collection PubMed
description We present an approach for variational regularization of inverse and imaging problems for recovering functions with values in a set of vectors. We introduce regularization functionals, which are derivative-free double integrals of such functions. These regularization functionals are motivated from double integrals, which approximate Sobolev semi-norms of intensity functions. These were introduced in Bourgain et al. (Another look at Sobolev spaces. In: Menaldi, Rofman, Sulem (eds) Optimal control and partial differential equations-innovations and applications: in honor of professor Alain Bensoussan’s 60th anniversary, IOS Press, Amsterdam, pp 439–455, 2001). For the proposed regularization functionals, we prove existence of minimizers as well as a stability and convergence result for functions with values in a set of vectors.
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spelling pubmed-66474952019-08-06 Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors Ciak, René Melching, Melanie Scherzer, Otmar J Math Imaging Vis Article We present an approach for variational regularization of inverse and imaging problems for recovering functions with values in a set of vectors. We introduce regularization functionals, which are derivative-free double integrals of such functions. These regularization functionals are motivated from double integrals, which approximate Sobolev semi-norms of intensity functions. These were introduced in Bourgain et al. (Another look at Sobolev spaces. In: Menaldi, Rofman, Sulem (eds) Optimal control and partial differential equations-innovations and applications: in honor of professor Alain Bensoussan’s 60th anniversary, IOS Press, Amsterdam, pp 439–455, 2001). For the proposed regularization functionals, we prove existence of minimizers as well as a stability and convergence result for functions with values in a set of vectors. Springer US 2019-02-07 2019 /pmc/articles/PMC6647495/ /pubmed/31396002 http://dx.doi.org/10.1007/s10851-018-00869-6 Text en © The Author(s) 2019 OpenAccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Ciak, René
Melching, Melanie
Scherzer, Otmar
Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title_full Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title_fullStr Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title_full_unstemmed Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title_short Regularization with Metric Double Integrals of Functions with Values in a Set of Vectors
title_sort regularization with metric double integrals of functions with values in a set of vectors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6647495/
https://www.ncbi.nlm.nih.gov/pubmed/31396002
http://dx.doi.org/10.1007/s10851-018-00869-6
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