DocumentCode :
695699
Title :
An EM approach for Poisson-Gaussian noise modeling
Author :
Jezierska, Anna ; Chaux, Caroline ; Pesquet, Jean-Christophe ; Talbot, Hugues
Author_Institution :
Lab. Inf. Gaspard Monge, Univ. Paris-Est, Marne-la-Vallée, France
fYear :
2011
fDate :
Aug. 29 2011-Sept. 2 2011
Firstpage :
2244
Lastpage :
2248
Abstract :
This paper deals with noise parameter estimation. We assume observations corrupted by noise modelled as a sum of two random processes: one Poisson and the other a (nonzero mean) Gaussian. Such problems arise in various applications, e.g. in astronomy and confocal microscopy imaging. To estimate noise parameters, we propose an iterative algorithm based on an Expectation-Maximization approach. This allows us to jointly estimate the scale parameter of the Poisson component and the mean and variance of the Gaussian one. Moreover, an adequate initialization based on cumulants is provided. Numerical difficulties arising from the procedure are also addressed. To validate the proposed method in terms of accuracy and robustness, tests are performed on synthetic data. The good performance of the method is also demonstrated in a denoising experiment on real data.
Keywords :
Gaussian processes; expectation-maximisation algorithm; iterative methods; parameter estimation; EM approach; Poisson component; Poisson-Gaussian noise modeling; expectation-maximization approach; iterative algorithm; noise parameter estimation; random process; Gaussian noise; Maximum likelihood estimation; Microscopy; Reliability; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2011 19th European
Conference_Location :
Barcelona
ISSN :
2076-1465
Type :
conf
Filename :
7074249
Link To Document :
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