author = "Bindilatti, Andr{\'e} de Andrade and Mascarenhas, Nelson Delfino 
          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 
             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 = "",
           targetfile = "nonlocal.pdf",
        urlaccessdate = "2021, Dec. 07"