Efficient parallelization on GPU of an image smoothing method based on a variational model
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s11554-016-0623-x http://hdl.handle.net/11449/168836 |
Resumo: | Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments. |
id |
UNSP_a1e341caf60fe30da8fea3698a752122 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/168836 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Efficient parallelization on GPU of an image smoothing method based on a variational modelCUDAGPGPUImage processingMultiplicative noiseMedical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments.Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Faculdade de Engenharia Universidade do PortoInstituto de Ciências Matemática e de Computação Universidade de São PauloDepartamento de Ciências da Computação Universidade Estadual Paulista-UNESPInstituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial Departamento de Engenharia Mecânica Faculdade de Engenharia Universidade do PortoDepartamento de Ciências da Computação Universidade Estadual Paulista-UNESPUniversidade do PortoUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Gulo, Carlos A. S. J.de Arruda, Henrique F.de Araujo, Alex F.Sementille, Antonio C. [UNESP]Tavares, João Manuel R. S.2018-12-11T16:43:17Z2018-12-11T16:43:17Z2016-07-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-13application/pdfhttp://dx.doi.org/10.1007/s11554-016-0623-xJournal of Real-Time Image Processing, p. 1-13.1861-8200http://hdl.handle.net/11449/16883610.1007/s11554-016-0623-x2-s2.0-849792208642-s2.0-84979220864.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Real-Time Image Processing0,322info:eu-repo/semantics/openAccess2023-09-30T06:01:37Zoai:repositorio.unesp.br:11449/168836Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:32:18.319018Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
title |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
spellingShingle |
Efficient parallelization on GPU of an image smoothing method based on a variational model Gulo, Carlos A. S. J. CUDA GPGPU Image processing Multiplicative noise |
title_short |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
title_full |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
title_fullStr |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
title_full_unstemmed |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
title_sort |
Efficient parallelization on GPU of an image smoothing method based on a variational model |
author |
Gulo, Carlos A. S. J. |
author_facet |
Gulo, Carlos A. S. J. de Arruda, Henrique F. de Araujo, Alex F. Sementille, Antonio C. [UNESP] Tavares, João Manuel R. S. |
author_role |
author |
author2 |
de Arruda, Henrique F. de Araujo, Alex F. Sementille, Antonio C. [UNESP] Tavares, João Manuel R. S. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Porto Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Gulo, Carlos A. S. J. de Arruda, Henrique F. de Araujo, Alex F. Sementille, Antonio C. [UNESP] Tavares, João Manuel R. S. |
dc.subject.por.fl_str_mv |
CUDA GPGPU Image processing Multiplicative noise |
topic |
CUDA GPGPU Image processing Multiplicative noise |
description |
Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-07-21 2018-12-11T16:43:17Z 2018-12-11T16:43:17Z |
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/10.1007/s11554-016-0623-x Journal of Real-Time Image Processing, p. 1-13. 1861-8200 http://hdl.handle.net/11449/168836 10.1007/s11554-016-0623-x 2-s2.0-84979220864 2-s2.0-84979220864.pdf |
url |
http://dx.doi.org/10.1007/s11554-016-0623-x http://hdl.handle.net/11449/168836 |
identifier_str_mv |
Journal of Real-Time Image Processing, p. 1-13. 1861-8200 10.1007/s11554-016-0623-x 2-s2.0-84979220864 2-s2.0-84979220864.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Real-Time Image Processing 0,322 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-13 application/pdf |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808128244183990272 |