A new bayesian Poisson denoising algorithm based on nonlocal means and stochastic distances

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorCentro Universitário Campo Limpo Paulista (UNIFACCAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.creatorEvangelista, Rodrigo C.-
Autor(es): dc.creatorSalvadeo, Denis H.P.-
Autor(es): dc.creatorMascarenhas, Nelson D.A.-
Data de aceite: dc.date.accessioned2025-08-21T16:21:31Z-
Data de disponibilização: dc.date.available2025-08-21T16:21:31Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2021.108363-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/222812-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/222812-
Descrição: dc.descriptionPoisson noise is the main cause of degradation of many imaging modalities. However, many of the proposed methods for reducing noise in images lack a formal approach. Our work develops a new, general, formal and computationally efficient bayesian Poisson denoising algorithm, based on the Nonlocal Means framework and replacing the euclidean distance by stochastic distances, which are more appropriate for the denoising problem. It takes advantage of the conjugacy of Poisson and gamma distributions to obtain its computational efficiency. When dealing with low dose CT images, the algorithm operates on the sinogram, modeling the rates of the Poisson noise by the Gamma distribution. Based on the Bayesian formulation and the conjugacy property, the likelihood follows the Poisson distribution, while the a posteriori distribution is also described by the Gamma distribution. The derived algorithm is applied to simulated and real low-dose CT images and compared to several algorithms proposed in the literature, with competitive results.-
Descrição: dc.descriptionCentro Universitário Campo Limpo Paulista (UNIFACCAMP)-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP)-
Descrição: dc.descriptionComputing Department Federal University of São Carlos (UFSCar)-
Descrição: dc.descriptionInstitute of Geosciences and Exact Sciences São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBayesian estimation-
Palavras-chave: dc.subjectConjugate distributions-
Palavras-chave: dc.subjectLow dose CT-
Palavras-chave: dc.subjectNonlocal means-
Palavras-chave: dc.subjectPoisson denoising-
Palavras-chave: dc.subjectStochastic distances-
Título: dc.titleA new bayesian Poisson denoising algorithm based on nonlocal means and stochastic distances-
Tipo de arquivo: dc.typetexto-
Aparece nas coleções:Repositório Institucional - Unesp

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