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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Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization
TL;DR: In this article, the authors adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms, and an accelerated version of the Parallel Proximal Algorithm is proposed to perform the minimization.
Journal Article
A Parallel Inertial Proximal Optimization Method
TL;DR: An extension of the Douglas-Rachford algorithm including inertia parameters is proposed and parallel versions to deal with the case of a sum of an arbitrary number of maximal operators are developed.
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Long-range dependence and heavy-tail modeling for teletraffic data
TL;DR: This tutorial article focuses on two such invariants related to the time dimension of the problem, namely, long-range dependence, or self-similarity, and heavy-tail marginal distributions.
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Image analysis using a dual-tree M-band wavelet transform
TL;DR: A two-dimensional generalization to the M-band case of the dual-tree decomposition structure based on a Hilbert pair of wavelets, which minimizes potential estimation errors and significant improvements in terms of both overall noise reduction and direction preservation are observed.
Posted Content
Parallel ProXimal Algorithm for Image Restoration Using Hybrid Regularization { Extended version
TL;DR: Numerical experiments performed in the context of Poisson data recovery, show the good behavior of the algorithm as well as promising results concerning the use of hybrid regularization techniques.