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Conference Papers Year : 2014

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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Dates and versions

hal-01060081 , version 1 (02-09-2014)
hal-01060081 , version 2 (18-09-2014)

Identifiers

  • HAL Id : hal-01060081 , version 2

Cite

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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