J
Jean-Christophe Pesquet
Researcher at Université Paris-Saclay
Publications - 387
Citations - 14714
Jean-Christophe Pesquet is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Convex optimization & Wavelet. The author has an hindex of 50, co-authored 364 publications receiving 13264 citations. Previous affiliations of Jean-Christophe Pesquet include University of Marne-la-Vallée & CentraleSupélec.
Papers
More filters
Journal ArticleDOI
Adaptive lifting scheme with sparse criteria for image coding
TL;DR: This article investigates techniques for optimizing sparsity criteria by focusing on the use of an ℓ1 criterion instead of an⁓2 one, and proposes to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights.
Journal ArticleDOI
An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension
Yosra Marnissi,Emilie Chouzenoux,Emilie Chouzenoux,A. Benazza-Benyahia,Jean-Christophe Pesquet +4 more
TL;DR: Experimental results indicate that adding the proposed auxiliary variables to the model makes the sampling problem simpler since the new conditional distribution no longer contains highly heterogeneous correlations, and the computational cost of each iteration of the Gibbs sampler is significantly reduced.
Posted Content
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
TL;DR: In this paper, the posterior density of the unknown parameters is derived in a variational Bayesian framework where the goal is to provide a good approximation to the posterior distribution in order to compute posterior mean estimates.
Proceedings ArticleDOI
Generalized multivariate exponential power prior for wavelet-based multichannel image restoration
TL;DR: This paper addresses the problem of recovering the image components in a wavelet domain by adopting a variational approach and addresses the challenging issue of computing the Maximum A Posteriori estimate by using a Majorize-Minimize optimization strategy.
Journal ArticleDOI
BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement
TL;DR: This work refines gene regulatory network (GRN) inference thanks to cluster information and provides additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database.