Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345816
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampNose-Filho, Kenji-
dc.contributor.authorunicampTakahata, André Kazuo-
dc.contributor.authorunicampLopes, Renato da Rocha-
dc.contributor.authorunicampRomano, João Marcos Travassos-
dc.typeArtigopt_BR
dc.titleA fast algorithm for sparse multichannel blind deconvolutionpt_BR
dc.contributor.authorNose-Filho, Kenji-
dc.contributor.authorTakahata, André K.-
dc.contributor.authorLopes, Renato-
dc.contributor.authorRomano, João M. T.-
dc.subjectDeconvoluçãopt_BR
dc.subject.otherlanguageDeconvolutionpt_BR
dc.description.abstractWe have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid l(1)/l(2)-norm loss function. This is in contrast to the SMBD, in which the hybrid l(1)/l(2)-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniquespt_BR
dc.relation.ispartofGeophysicspt_BR
dc.publisher.cityTulsa, OKpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherSociety of Exploration Geophysicistspt_BR
dc.date.issued2016-
dc.date.monthofcirculationJan.pt_BR
dc.language.isoengpt_BR
dc.description.volume81pt_BR
dc.description.issuenumber1pt_BR
dc.description.firstpageV7pt_BR
dc.description.lastpageV16pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0016-8033pt_BR
dc.identifier.eissn1942-2156pt_BR
dc.identifier.doi10.1190/GEO2015-0069.1pt_BR
dc.identifier.urlhttps://library.seg.org/doi/full/10.1190/geo2015-0069.1pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsordocumentnumbernão tempt_BR
dc.description.sponsordocumentnumbernão tempt_BR
dc.date.available2020-07-21T13:26:15Z-
dc.date.accessioned2020-07-21T13:26:15Z-
dc.description.provenanceSubmitted by Mariana Aparecida Azevedo (mary1@unicamp.br) on 2020-07-21T13:26:15Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-08T19:02:16Z : No. of bitstreams: 1 000377880100019.pdf: 3302348 bytes, checksum: c7e861950e93922bb4c57c867ac141f2 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-07-21T13:26:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2016en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/345816-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Comunicaçõespt_BR
dc.contributor.departmentDepartamento de Comunicaçõespt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.subject.keywordBayesian approachpt_BR
dc.subject.keywordFast algorithmpt_BR
dc.identifier.source000377880100019pt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcid0000-0003-2618-5499pt_BR
dc.creator.orcid0000-0002-2159-160Xpt_BR
dc.type.formArtigo técnicopt_BR
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