A Deep Boltzmann machine-based approach for robust image denoising

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dc.contributor.authorPires, Rafael G.
dc.contributor.authorSantos, Daniel S. [UNESP]
dc.contributor.authorSouza, Gustavo B.
dc.contributor.authorMarana, Aparecido N. [UNESP]
dc.contributor.authorLevada, Alexandre L. M.
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:36:00Z
dc.date.available2018-12-11T17:36:00Z
dc.date.issued2018-01-01
dc.description.abstractA Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Machines (RBMs), which correspond to a specific kind of stochastic energy-based networks. In this work, a DBM is applied to a robust image denoising by minimizing the contribution of some of its top nodes, called “noise nodes”, which often get excited when noise pixels are present in the given images. After training the DBM with noise and clean images, the detection and deactivation of the noise nodes allow reconstructing images with great quality, eliminating most of their noise. The results obtained from important public image datasets showed the validity of the proposed approach.en
dc.description.affiliationDepartment of Computing UFSCar - Federal University of São Carlos
dc.description.affiliationDepartment of Computing UNESP - Univ Estadual Paulista, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01
dc.description.affiliationUnespDepartment of Computing UNESP - Univ Estadual Paulista, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: #2014/12236-1
dc.description.sponsorshipIdFAPESP: #2014/16250-9
dc.description.sponsorshipIdFAPESP: #2016/19403-6
dc.description.sponsorshipIdCAPES: #306166/2014-3
dc.description.sponsorshipIdCNPq: #306166/2014-3
dc.format.extent525-533
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-75193-1_63
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 525-533.
dc.identifier.doi10.1007/978-3-319-75193-1_63
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85042230686
dc.identifier.urihttp://hdl.handle.net/11449/179605
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.titleA Deep Boltzmann machine-based approach for robust image denoisingen
dc.typeTrabalho apresentado em evento
unesp.author.lattes6027713750942689[4]
unesp.author.orcid0000-0001-9597-055X[1]
unesp.author.orcid0000-0002-4441-9108[3]
unesp.author.orcid0000-0003-4861-7061[4]
unesp.author.orcid0000-0002-6494-7514[6]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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