Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions

Detalhes bibliográficos
Autor(a) principal: Salvadeo, Denis H. P. [UNESP]
Data de Publicação: 2014
Outros Autores: Bloch, Isabelle, Tupin, Florence, Mascarenhas, Nelson D. A., Levada, Alexandre L. M., Deledalle, Charles-Alban, Dahdouh, Sonia
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICIP.2014.7025546
http://hdl.handle.net/11449/228083
Resumo: In this paper, an extension of the framework proposed by Deledalle et al. [1] for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.
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spelling Denoising based on non local means for ultrasound images with simultaneous multiple noise distributionsimage denoisingmultiple noisesnon local meansultrasound imageultrasound segmentationIn this paper, an extension of the framework proposed by Deledalle et al. [1] for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.São Paulo State University DEMACFederal University of São Carlos Computer DepartmentInstitut Mines-Télécom Télécom ParisTech CNRS LTCIUniversité Bordeaux 1 Institut de Mathématiques de BordeauxSão Paulo State University DEMACUniversidade Estadual Paulista (UNESP)Universidade Federal de São Carlos (UFSCar)CNRS LTCIInstitut de Mathématiques de BordeauxSalvadeo, Denis H. P. [UNESP]Bloch, IsabelleTupin, FlorenceMascarenhas, Nelson D. A.Levada, Alexandre L. M.Deledalle, Charles-AlbanDahdouh, Sonia2022-04-29T07:26:35Z2022-04-29T07:26:35Z2014-01-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2699-2703http://dx.doi.org/10.1109/ICIP.2014.70255462014 IEEE International Conference on Image Processing, ICIP 2014, p. 2699-2703.http://hdl.handle.net/11449/22808310.1109/ICIP.2014.70255462-s2.0-84949929075Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2014 IEEE International Conference on Image Processing, ICIP 2014info:eu-repo/semantics/openAccess2022-04-29T07:26:35Zoai:repositorio.unesp.br:11449/228083Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T07:26:35Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
title Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
spellingShingle Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
Salvadeo, Denis H. P. [UNESP]
image denoising
multiple noises
non local means
ultrasound image
ultrasound segmentation
title_short Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
title_full Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
title_fullStr Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
title_full_unstemmed Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
title_sort Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
author Salvadeo, Denis H. P. [UNESP]
author_facet Salvadeo, Denis H. P. [UNESP]
Bloch, Isabelle
Tupin, Florence
Mascarenhas, Nelson D. A.
Levada, Alexandre L. M.
Deledalle, Charles-Alban
Dahdouh, Sonia
author_role author
author2 Bloch, Isabelle
Tupin, Florence
Mascarenhas, Nelson D. A.
Levada, Alexandre L. M.
Deledalle, Charles-Alban
Dahdouh, Sonia
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Federal de São Carlos (UFSCar)
CNRS LTCI
Institut de Mathématiques de Bordeaux
dc.contributor.author.fl_str_mv Salvadeo, Denis H. P. [UNESP]
Bloch, Isabelle
Tupin, Florence
Mascarenhas, Nelson D. A.
Levada, Alexandre L. M.
Deledalle, Charles-Alban
Dahdouh, Sonia
dc.subject.por.fl_str_mv image denoising
multiple noises
non local means
ultrasound image
ultrasound segmentation
topic image denoising
multiple noises
non local means
ultrasound image
ultrasound segmentation
description In this paper, an extension of the framework proposed by Deledalle et al. [1] for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-28
2022-04-29T07:26:35Z
2022-04-29T07:26:35Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ICIP.2014.7025546
2014 IEEE International Conference on Image Processing, ICIP 2014, p. 2699-2703.
http://hdl.handle.net/11449/228083
10.1109/ICIP.2014.7025546
2-s2.0-84949929075
url http://dx.doi.org/10.1109/ICIP.2014.7025546
http://hdl.handle.net/11449/228083
identifier_str_mv 2014 IEEE International Conference on Image Processing, ICIP 2014, p. 2699-2703.
10.1109/ICIP.2014.7025546
2-s2.0-84949929075
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2014 IEEE International Conference on Image Processing, ICIP 2014
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 2699-2703
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_ 1799965738874699776

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