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Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC) banon
Metadata Last Update2016: (UTC) administrator
Citation KeyBindilattiMasc:2015:NoApPo
TitleNonlocal approaches for Poisson noise removal
Access Date2022, Jan. 28
Secondary TypeMaster's Work
Number of Files1
Size30 KiB
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Author1 Bindilatti, André de Andrade
2 Mascarenhas, Nelson Delfino d'Ávila
Affiliation1 Federal University of São Carlos
2 Federal University of São Carlos
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-07-31 17:04:22 :: -> administrator ::
2015-09-09 02:54:45 :: administrator -> banon :: 2015
2015-09-09 02:59:07 :: banon -> administrator :: 2015
2016-06-03 21:18:38 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
stochastic distances
Poisson noise
image denoising
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 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. > SDLA > SIBGRAPI 2015 > Nonlocal approaches for...
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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