@InProceedings{BindilattiMasc:2015:NoApPo,
author = "Bindilatti, Andr{\'e} de Andrade and Mascarenhas, Nelson Delfino
d'{\'A}vila",
affiliation = "{Federal University of S{\~a}o Carlos} and {Federal University of
S{\~a}o Carlos}",
title = "Nonlocal approaches for Poisson noise removal",
booktitle = "Proceedings...",
year = "2015",
editor = "Segundo, Maur{\'{\i}}cio Pamplona and Faria, Fabio Augusto",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "nonlocal-means, stochastic distances, Poisson noise, image
denoising.",
abstract = "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 acquired 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. NLM was originally proposed for additive
noise reduction. The goal of this research was 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. Since
stochastic distances assume a parametric model for the data
distribution, knowledge of the model parameters is necessary. We
have proposed two approaches to estimate the model parameters, a
two-stage algorithm and an iterative approach. The experiment
results demonstrate that the proposed approaches are competitive
with respect to the state-of-the-art algorithms.",
conference-location = "Salvador",
conference-year = "Aug. 26-29, 2015",
language = "en",
ibi = "8JMKD3MGPBW34M/3JUHHGH",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JUHHGH",
targetfile = "nonlocal.pdf",
urlaccessdate = "2021, Dec. 07"
}