DEEP REGRESSOR NETWORKS FOR BLIND IMAGE DEBLURRING

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPires, Rafael G.-
Autor(es): dc.creatorSantos, Daniel F.S.-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2025-08-21T23:47:40Z-
Data de disponibilização: dc.date.available2025-08-21T23:47:40Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IGARSS47720.2021.9554775-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241822-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241822-
Descrição: dc.descriptionImage restoration concerns mainly smoothing noise and deblurring images that were corrupted either during acquisition or transmission. Since traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the deblurring process, image blur classification and further parameter estimation are critical for blind image deblurring. This paper tackles the problem in three steps: (i) it first identifies the blur type for each input image, (ii) then it estimates the respective kernel parameter, and (iii) finally, it uses deconvolution filters to restore the blurred image. The proposed approach, called Deep Regressor Networks, showed promising results in general-purpose and remote sensing image datasets corrupted by different types and blur levels than some state-of-the-art techniques.-
Descrição: dc.descriptionSão Paulo State University UNESP Department of Computing, SP-
Descrição: dc.descriptionSão Paulo State University UNESP Department of Computing, SP-
Formato: dc.format5390-5393-
Idioma: dc.languageen-
Relação: dc.relationInternational Geoscience and Remote Sensing Symposium (IGARSS)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBlind Deconvolution-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectImage Restoration-
Palavras-chave: dc.subjectRemote sensing-
Título: dc.titleDEEP REGRESSOR NETWORKS FOR BLIND IMAGE DEBLURRING-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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