dc.contributorMascarenhas, Nelson Delfino d'Ávila
dc.contributorhttp://lattes.cnpq.br/0557976975338451
dc.contributorhttp://lattes.cnpq.br/9514883575909296
dc.creatorBindilatti, André de Andrade
dc.date.accessioned2014-10-31
dc.date.accessioned2016-06-02T19:06:17Z
dc.date.available2014-10-31
dc.date.available2016-06-02T19:06:17Z
dc.date.created2014-10-31
dc.date.created2016-06-02T19:06:17Z
dc.date.issued2014-05-23
dc.identifierBINDILATTI, André de Andrade. Abordagens não-locais para filtragem de ruído Poisson. 2014. 96 f. Dissertação (Mestrado em Ciências Exatas e da Terra) - Universidade Federal de São Carlos, São Carlos, 2014.
dc.identifierhttps://repositorio.ufscar.br/handle/ufscar/580
dc.description.abstractA common problem to applications such as positron emission tomography, low-exposure X-ray imaging, fluorescence microscopy, optical and infrared astronomy, and others, is the degradation of the original signal by Poisson Noise. This problem arises in applications in which the image acquisition process is based on counting photons reaching a detector surface during a given exposure time. Recently, a new algorithm for image denoising, called Nonlocal-Means (NLM), was proposed. The NLM algorithm consists of a nonlocal approach that explores the inherent image redundancy for denoising, that is, it explores the principle in which, in natural images, there are similar regions, yet locally disjoint. NLM was originally proposed for additive noise reduction. The goal of this work is to extend the NLM algorithm for Poisson noise filtering. To achieve this goal, symmetric divergences, also known as stochastic distances, have been applied as similarity metrics to the NLM algorithm. Stochastic distances assume a parametric model for the data distribution. Therefore they can accommodate different stochastic noise models. However, knowledge of the model parameters is necessary to calculate the stochastic distances. In this research, estimation and non-local filtering schemes were considered under Poisson noise hypothesis, leading to competitive results with the state of- the-art.
dc.publisherUniversidade Federal de São Carlos
dc.publisherBR
dc.publisherUFSCar
dc.publisherPrograma de Pós-Graduação em Ciência da Computação - PPGCC
dc.rightsAcesso Aberto
dc.subjectProcessamento de imagens
dc.subjectRestauração de imagens
dc.subjectRuído Poisson
dc.subjectFiltragem de ruído
dc.subjectImage processing
dc.subjectImage restoration
dc.subjectNonlocal-means
dc.subjectPoisson noise
dc.subjectImage denoising
dc.subjectStochastic distances
dc.titleAbordagens não-locais para filtragem de ruído Poisson
dc.typeTesis


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