A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising

Detalhes bibliográficos
Autor(a) principal: Pires, Rafael G.
Data de Publicação: 2019
Outros Autores: Santos, Daniel S. [UNESP], Souza, Gustavo B., Levada, Alexandre L. M., Papa, João Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-030-30493-5_46
http://hdl.handle.net/11449/201210
Resumo: During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.
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spelling A Sparse Filtering-Based Approach for Non-blind Deep Image DenoisingDuring the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.UFSCar - Federal University of São CarlosUNESP - São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01UNESP - São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Pires, Rafael G.Santos, Daniel S. [UNESP]Souza, Gustavo B.Levada, Alexandre L. M.Papa, João Paulo [UNESP]2020-12-12T02:26:53Z2020-12-12T02:26:53Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject471-482http://dx.doi.org/10.1007/978-3-030-30493-5_46Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11731 LNCS, p. 471-482.1611-33490302-9743http://hdl.handle.net/11449/20121010.1007/978-3-030-30493-5_462-s2.0-85072959140Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/201210Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:36:38.547814Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
title A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
spellingShingle A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
Pires, Rafael G.
title_short A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
title_full A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
title_fullStr A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
title_full_unstemmed A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
title_sort A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising
author Pires, Rafael G.
author_facet Pires, Rafael G.
Santos, Daniel S. [UNESP]
Souza, Gustavo B.
Levada, Alexandre L. M.
Papa, João Paulo [UNESP]
author_role author
author2 Santos, Daniel S. [UNESP]
Souza, Gustavo B.
Levada, Alexandre L. M.
Papa, João Paulo [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Carlos (UFSCar)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Pires, Rafael G.
Santos, Daniel S. [UNESP]
Souza, Gustavo B.
Levada, Alexandre L. M.
Papa, João Paulo [UNESP]
description During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
2020-12-12T02:26:53Z
2020-12-12T02:26:53Z
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/10.1007/978-3-030-30493-5_46
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11731 LNCS, p. 471-482.
1611-3349
0302-9743
http://hdl.handle.net/11449/201210
10.1007/978-3-030-30493-5_46
2-s2.0-85072959140
url http://dx.doi.org/10.1007/978-3-030-30493-5_46
http://hdl.handle.net/11449/201210
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11731 LNCS, p. 471-482.
1611-3349
0302-9743
10.1007/978-3-030-30493-5_46
2-s2.0-85072959140
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 471-482
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_ 1808129096308228096

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