Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TIP.2017.2699483 http://hdl.handle.net/11449/159559 |
Resumo: | Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the l(1)-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction. |
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Repositório Institucional da UNESP |
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Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse ReconstructionsTomographic image reconstructioniterative shrinkage-thresholdingline searchRecently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the l(1)-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Tecnol Fed Parana, BR-80230901 Curitiba, Parana, BrazilState Univ Sao Paulo Sao Carlos, BR-13566590 Sao Carlos, SP, BrazilState Univ Sao Paulo Sao Carlos, BR-13566590 Sao Carlos, SP, BrazilCNPq: 475553/2013-6CNPq: 311476/2014-7CNPq: 312023/2015-4FAPESP: 2013/07375-0Ieee-inst Electrical Electronics Engineers IncUniv Tecnol Fed ParanaUniversidade Estadual Paulista (Unesp)Zibetti, Marcelo V. W.Helou, Elias S. [UNESP]Pipa, Daniel. R.2018-11-26T15:44:17Z2018-11-26T15:44:17Z2017-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3569-3578application/pdfhttp://dx.doi.org/10.1109/TIP.2017.2699483Ieee Transactions On Image Processing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 26, n. 7, p. 3569-3578, 2017.1057-7149http://hdl.handle.net/11449/15955910.1109/TIP.2017.2699483WOS:000402136500021WOS000402136500021.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Image Processing1,374info:eu-repo/semantics/openAccess2023-12-06T06:18:27Zoai:repositorio.unesp.br:11449/159559Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:37:54.107368Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
title |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
spellingShingle |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions Zibetti, Marcelo V. W. Tomographic image reconstruction iterative shrinkage-thresholding line search |
title_short |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
title_full |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
title_fullStr |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
title_full_unstemmed |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
title_sort |
Accelerating Overrelaxed and Monotone Fast Iterative Shrinkage-Thresholding Algorithms With Line Search for Sparse Reconstructions |
author |
Zibetti, Marcelo V. W. |
author_facet |
Zibetti, Marcelo V. W. Helou, Elias S. [UNESP] Pipa, Daniel. R. |
author_role |
author |
author2 |
Helou, Elias S. [UNESP] Pipa, Daniel. R. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Univ Tecnol Fed Parana Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Zibetti, Marcelo V. W. Helou, Elias S. [UNESP] Pipa, Daniel. R. |
dc.subject.por.fl_str_mv |
Tomographic image reconstruction iterative shrinkage-thresholding line search |
topic |
Tomographic image reconstruction iterative shrinkage-thresholding line search |
description |
Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the l(1)-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-01 2018-11-26T15:44:17Z 2018-11-26T15:44:17Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TIP.2017.2699483 Ieee Transactions On Image Processing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 26, n. 7, p. 3569-3578, 2017. 1057-7149 http://hdl.handle.net/11449/159559 10.1109/TIP.2017.2699483 WOS:000402136500021 WOS000402136500021.pdf |
url |
http://dx.doi.org/10.1109/TIP.2017.2699483 http://hdl.handle.net/11449/159559 |
identifier_str_mv |
Ieee Transactions On Image Processing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 26, n. 7, p. 3569-3578, 2017. 1057-7149 10.1109/TIP.2017.2699483 WOS:000402136500021 WOS000402136500021.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Transactions On Image Processing 1,374 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
3569-3578 application/pdf |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129098336174080 |