Iterative Poisson-Gaussian Noise Parametric Estimation for Blind Image Denoising

Anna Jezierska 1 Jean-Christophe Pesquet 1 Hugues Talbot 1 Caroline Chaux 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 LATP - Laboratoire d'Analyse, Topologie, Probabilités
Abstract : This paper deals with noise parameter estimation from a single im- age under Poisson-Gaussian noise statistics. The problem is formu- lated within a mixed discrete-continuous optimization framework. The proposed approach jointly estimates the signal of interest and the noise parameters. This is achieved by introducing an adjustable reg- ularization term inside an optimized criterion, together with a data fidelity error measure. The optimal solution is sought iteratively by alternating the minimization of a label field and of a noise param- eter vector. Noise parameters are updated at each iteration using an Expectation-Maximization approach. The proposed algorithm is inspired from a spatial regularization approach for vector quantiza- tion. We illustrate the usefulness of our approach on macroconfocal images. The identified noise parameters are applied to a denoising algorithm, so yielding a fully automatic denoising scheme.
Mots-clés : Poisson-Gaussian noise parameter estimation Expectation-Maximization proximal algorithms discrete optimization
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Communication dans un congrès
IEEE International Conference on Image Processing, Oct 2014, Paris, France. pp.1-5, 2014
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Anna Jezierska, Jean-Christophe Pesquet, Hugues Talbot, Caroline Chaux. Iterative Poisson-Gaussian Noise Parametric Estimation for Blind Image Denoising. IEEE International Conference on Image Processing, Oct 2014, Paris, France. pp.1-5, 2014. 〈hal-01060081v2〉

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