Abordagens não-locais para filtragem de ruído Poisson

• Ano de defesa: 2014
• Autor(a) principal: Bindilatti, André de Andrade
• Orientador(a): Mascarenhas, Nelson Delfino d'Ávila
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Processamento de imagens; Restauração de imagens; Ruído Poisson; Filtragem de ruído
• Palavras-chave em Inglês: Image processing; Image restoration; Nonlocal-means; Poisson noise; Image denoising; Stochastic distances
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/580

A 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.