A Novel Memory-Efficient Fast Algorithm for 2-D
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.19/2502 |
Resumo: | The basic theories of compressed sensing (CS) turn around the sampling and reconstruction of 1-D signals. To deal with 2-D signals (images), the conventional treatment is to convert them into1-D vectors. This has drawbacks, including huge memory demands and difficulties in the design and calibration of the optical imaging systems. As a result, in 2009 some researchers proposed the concept of compressed imaging (CI) with separable sensing operators. However, their work is only focused on the sampling phase. In this paper, we propose a scheme for 2-D CS that is memory- and computation-efficient in both sampling and reconstruction. This is achieved by decomposing the 2-D CS problem into two stages with the help of an intermediate image. The intermediate image is then solved by direct orthogonal linear transform and the original image is reconstructed by solving a set of 1-D l1-norm minimization sub-problems. The experimental results confirm the feasibility of the proposed scheme. |
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A Novel Memory-Efficient Fast Algorithm for 2-DCompressed sensing2-D transformImage reconstructionImaging samplingThe basic theories of compressed sensing (CS) turn around the sampling and reconstruction of 1-D signals. To deal with 2-D signals (images), the conventional treatment is to convert them into1-D vectors. This has drawbacks, including huge memory demands and difficulties in the design and calibration of the optical imaging systems. As a result, in 2009 some researchers proposed the concept of compressed imaging (CI) with separable sensing operators. However, their work is only focused on the sampling phase. In this paper, we propose a scheme for 2-D CS that is memory- and computation-efficient in both sampling and reconstruction. This is achieved by decomposing the 2-D CS problem into two stages with the help of an intermediate image. The intermediate image is then solved by direct orthogonal linear transform and the original image is reconstructed by solving a set of 1-D l1-norm minimization sub-problems. The experimental results confirm the feasibility of the proposed scheme.This work is supported in part by FCT, Portugal, under Ciência 2007 program, the National Natural Science Foundation of China under grant no. 60872119 and the Natural Science Foundation of Shandong Province under grant no. 2009ZRB01675IEEERepositório Científico do Instituto Politécnico de ViseuWang, HuiyuanVieira, JoséJesus, BrunoDuarte, IsabelFerreira, Paulo2015-01-08T09:38:36Z2010-062010-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.19/2502eng978-1-4244-6712-910.1109/WCICA.2010.5553848metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T15:25:45Zoai:repositorio.ipv.pt:10400.19/2502Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:41:38.777645Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Novel Memory-Efficient Fast Algorithm for 2-D |
title |
A Novel Memory-Efficient Fast Algorithm for 2-D |
spellingShingle |
A Novel Memory-Efficient Fast Algorithm for 2-D Wang, Huiyuan Compressed sensing 2-D transform Image reconstruction Imaging sampling |
title_short |
A Novel Memory-Efficient Fast Algorithm for 2-D |
title_full |
A Novel Memory-Efficient Fast Algorithm for 2-D |
title_fullStr |
A Novel Memory-Efficient Fast Algorithm for 2-D |
title_full_unstemmed |
A Novel Memory-Efficient Fast Algorithm for 2-D |
title_sort |
A Novel Memory-Efficient Fast Algorithm for 2-D |
author |
Wang, Huiyuan |
author_facet |
Wang, Huiyuan Vieira, José Jesus, Bruno Duarte, Isabel Ferreira, Paulo |
author_role |
author |
author2 |
Vieira, José Jesus, Bruno Duarte, Isabel Ferreira, Paulo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Viseu |
dc.contributor.author.fl_str_mv |
Wang, Huiyuan Vieira, José Jesus, Bruno Duarte, Isabel Ferreira, Paulo |
dc.subject.por.fl_str_mv |
Compressed sensing 2-D transform Image reconstruction Imaging sampling |
topic |
Compressed sensing 2-D transform Image reconstruction Imaging sampling |
description |
The basic theories of compressed sensing (CS) turn around the sampling and reconstruction of 1-D signals. To deal with 2-D signals (images), the conventional treatment is to convert them into1-D vectors. This has drawbacks, including huge memory demands and difficulties in the design and calibration of the optical imaging systems. As a result, in 2009 some researchers proposed the concept of compressed imaging (CI) with separable sensing operators. However, their work is only focused on the sampling phase. In this paper, we propose a scheme for 2-D CS that is memory- and computation-efficient in both sampling and reconstruction. This is achieved by decomposing the 2-D CS problem into two stages with the help of an intermediate image. The intermediate image is then solved by direct orthogonal linear transform and the original image is reconstructed by solving a set of 1-D l1-norm minimization sub-problems. The experimental results confirm the feasibility of the proposed scheme. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-06 2010-06-01T00:00:00Z 2015-01-08T09:38:36Z |
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://hdl.handle.net/10400.19/2502 |
url |
http://hdl.handle.net/10400.19/2502 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
978-1-4244-6712-9 10.1109/WCICA.2010.5553848 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130882763653120 |