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Iterative Poisson-Gaussian Noise Parametric Estimation for Blind Image Denoising

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.
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Contributor : Anna Jezierska <>
Submitted on : Thursday, September 18, 2014 - 9:57:01 AM
Last modification on : Thursday, February 4, 2021 - 3:09:40 AM
Long-term archiving on: : Friday, December 19, 2014 - 11:37:04 AM


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  • HAL Id : hal-01060081, version 2


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. ⟨hal-01060081v2⟩



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