HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Thursday, September 22, 2016 - 4:38:18 PM
Last modification on : Saturday, January 15, 2022 - 3:57:43 AM



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⟩



Record views