Denoising based on non local means for ultrasound images with simultaneous multiple noise distributions
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , , , |
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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Repositório Institucional da UNESP |
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2946 |
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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:29462024-08-06T00:04:25.288325Repositório 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_ |
1808129579936645120 |