Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian noise

Abstract : This paper presents a new method for solving linear inverse problems where the observations are corrupted with a mixed Poisson-Gaussian noise. To generate a reliable solution, a regularized approach is often adopted in the literature. In this context, the optimal selection of the regularization parameters is of crucial importance in terms of estimation performance. The variational Bayesian-based approach we propose in this work allows us to automatically estimate the original signal and the associated regularization parameter from the observed data. A majorization-minimization technique is employed to circumvent the difficulties raised by the intricate form of the Poisson-Gaussian likelihood. Experimental results show that the proposed method is fast and achieves state-of-the art performance in comparison with approaches where the regularization parameters are manually adjusted.
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Conference papers
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Contributor : Emilie Chouzenoux <>
Submitted on : Thursday, September 22, 2016 - 4:38:18 PM
Last modification on : Thursday, July 5, 2018 - 2:45:50 PM

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Yosra Marnissi, Yuling Zheng, Jean-Christophe Pesquet. Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian noise. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2016, Shanghai, China. p. 3964-3968, ⟨10.1109/icassp.2016.7472421 ⟩. ⟨hal-01370499⟩

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