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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.
Cited literature [21 references]
https://hal.archives-ouvertes.fr/hal-01060081
Contributor : Anna Jezierska Connect in order to contact the contributor
Submitted on : Thursday, September 18, 2014 - 9:57:01 AM
Last modification on : Saturday, January 15, 2022 - 3:56:09 AM
Long-term archiving on: : Friday, December 19, 2014 - 11:37:04 AM
Contributor : Anna Jezierska Connect in order to contact the contributor
Submitted on : Thursday, September 18, 2014 - 9:57:01 AM
Last modification on : Saturday, January 15, 2022 - 3:56:09 AM
Long-term archiving on: : Friday, December 19, 2014 - 11:37:04 AM
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