@inproceedings{Jezierska_2011_eusipco,
 author = {Jezierska, A. and Chaux, C. and Pesquet, J.-C. and Talbot, H.},
 title = {An {EM} approach for {P}oisson-{G}aussian noise modeling},
 booktitle = {European Signal Processing Conference (EUSIPCO)},
 year = {2011},
 month = {Aug.},
 address = {Barcelona},
 pages = {2244--2248},
 numpages = {5},
 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.}
}