A sparse coding approach to inverse problems with application to microwave tomography

Autores
Caiafa, César Federico; Irastorza, Ramiro Miguel
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies. To reconstruct images from incomplete and distorted data, it is necessary to create algorithms that can take into account both, the physical mechanisms responsible for generating these measurements and the intrinsic characteristics of the images being analyzed. In this work, the sparse representation of images is reviewed, which is a realistic, compact and effective generative model for natural images inspired by the visual system of mammals. It enables us to address ill-posed linear inverse problems by training the model on a vast collection of images. Moreover, we extend the application of sparse coding to solve the non-linear and ill-posed problem in micr
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Irastorza, Ramiro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina
IAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South America
Buenos Aires
Argentina
Instituto Argentino de Astronomía
Materia
Microwave
Tomography
Sparse
imaging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/221889
Acceder
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spelling A sparse coding approach to inverse problems with application to microwave tomographyCaiafa, César FedericoIrastorza, Ramiro MiguelMicrowaveTomographySparseimaginghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies. To reconstruct images from incomplete and distorted data, it is necessary to create algorithms that can take into account both, the physical mechanisms responsible for generating these measurements and the intrinsic characteristics of the images being analyzed. In this work, the sparse representation of images is reviewed, which is a realistic, compact and effective generative model for natural images inspired by the visual system of mammals. It enables us to address ill-posed linear inverse problems by training the model on a vast collection of images. Moreover, we extend the application of sparse coding to solve the non-linear and ill-posed problem in micrFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Irastorza, Ramiro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaIAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South AmericaBuenos AiresArgentinaInstituto Argentino de AstronomíaUniversidad Autónoma de México2023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectWorkshopJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/221889A sparse coding approach to inverse problems with application to microwave tomography; IAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South America; Buenos Aires; Argentina; 2022; 1-51405-2059CONICET DigitalCONICETenghttps://congresos.unlp.edu.ar/iar60ws/https://congresos.unlp.edu.ar/iar60ws/speakers/https://drive.google.com/file/d/1Jmq7OhnoLSeH9WoH7HxYmw9ytivUsx_c/viewinfo:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2308.03818Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:10:32Zoai:ri.conicet.gov.ar:11336/221889instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:10:33.079CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A sparse coding approach to inverse problems with application to microwave tomography
title A sparse coding approach to inverse problems with application to microwave tomography
spellingShingle A sparse coding approach to inverse problems with application to microwave tomography
Caiafa, César Federico
Microwave
Tomography
Sparse
imaging
title_short A sparse coding approach to inverse problems with application to microwave tomography
title_full A sparse coding approach to inverse problems with application to microwave tomography
title_fullStr A sparse coding approach to inverse problems with application to microwave tomography
title_full_unstemmed A sparse coding approach to inverse problems with application to microwave tomography
title_sort A sparse coding approach to inverse problems with application to microwave tomography
dc.creator.none.fl_str_mv Caiafa, César Federico
Irastorza, Ramiro Miguel
author Caiafa, César Federico
author_facet Caiafa, César Federico
Irastorza, Ramiro Miguel
author_role author
author2 Irastorza, Ramiro Miguel
author2_role author
dc.subject.none.fl_str_mv Microwave
Tomography
Sparse
imaging
topic Microwave
Tomography
Sparse
imaging
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies. To reconstruct images from incomplete and distorted data, it is necessary to create algorithms that can take into account both, the physical mechanisms responsible for generating these measurements and the intrinsic characteristics of the images being analyzed. In this work, the sparse representation of images is reviewed, which is a realistic, compact and effective generative model for natural images inspired by the visual system of mammals. It enables us to address ill-posed linear inverse problems by training the model on a vast collection of images. Moreover, we extend the application of sparse coding to solve the non-linear and ill-posed problem in micr
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Irastorza, Ramiro Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina
IAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South America
Buenos Aires
Argentina
Instituto Argentino de Astronomía
description Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies. To reconstruct images from incomplete and distorted data, it is necessary to create algorithms that can take into account both, the physical mechanisms responsible for generating these measurements and the intrinsic characteristics of the images being analyzed. In this work, the sparse representation of images is reviewed, which is a realistic, compact and effective generative model for natural images inspired by the visual system of mammals. It enables us to address ill-posed linear inverse problems by training the model on a vast collection of images. Moreover, we extend the application of sparse coding to solve the non-linear and ill-posed problem in micr
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Workshop
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/221889
A sparse coding approach to inverse problems with application to microwave tomography; IAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South America; Buenos Aires; Argentina; 2022; 1-5
1405-2059
CONICET Digital
CONICET
url http://hdl.handle.net/11336/221889
identifier_str_mv A sparse coding approach to inverse problems with application to microwave tomography; IAR 60th Anniversary: Prospects for Low Frequency Radio Astronomy in South America; Buenos Aires; Argentina; 2022; 1-5
1405-2059
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://congresos.unlp.edu.ar/iar60ws/
https://congresos.unlp.edu.ar/iar60ws/speakers/
https://drive.google.com/file/d/1Jmq7OhnoLSeH9WoH7HxYmw9ytivUsx_c/view
info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2308.03818
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv Universidad Autónoma de México
publisher.none.fl_str_mv Universidad Autónoma de México
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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