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
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Proceedings ArticleDOI
Estimating long-range dependence in impulsive traffic flows
TL;DR: This work proposes an estimator for the generalized codifference and provides the conditions for it to be asymptotically consistent and shows that these conditions are satisfied for the EAFRP, which is a process proposed for modeling high-speed network traffic.
Proceedings ArticleDOI
Seismic multiple removal with a primal-dual proximal algorithm
TL;DR: This paper recast the problem of jointly estimating the filters and the signal of interest (primary) in a new convex variational formulation, allowing the incorporation of knowledge about the noise statistics, and designs a primal-dual algorithm which yields good performance in the provided simulation examples.
Journal Article
Preconditioned P-ULA for Joint Deconvolution-Segmentation of Ultrasound Images - Extended Version
Marie-Caroline Corbineau,Denis Kouame,Emilie Chouzenoux,Jean-Yves Tourneret,Jean-Christophe Pesquet +4 more
TL;DR: In this paper, an accelerated Markov chain Monte Carlo (MCMC) method is proposed for joint deconvolution and segmentation of ultrasound images, where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm.
Journal ArticleDOI
Neural Speed-Torque Estimator for Induction Motors in the Presence of Measurement Noise
TL;DR: A neural network approach is introduced to estimate non-noisy speed and torque from noisy measured currents and voltages in induction motors with Variable Speed Drives and it is shown that the proposed joint denoising-estimation strategy performs very well on real data benchmarks.
Journal ArticleDOI
A Proximal Decomposition Method for Solving Convex Variational Inverse Problems
TL;DR: In this article, a proximal decomposition algorithm for inverse problems with an arbitrary number of nonsmooth functions is proposed and the algorithm is shown to converge to the optimal solution.