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
Proceedings ArticleDOI
Adaptive lifting schemes with a global ℓ 1 minimization technique for image coding
TL;DR: This paper presents a sparse optimization technique based on recent convex algorithms and applied to the prediction filters of a two-dimensional non separable lifting structure that leads to a new optimization criterion taking into account linear dependencies between the generated coefficients.
Posted Content
4D Wavelet-Based Regularization for Parallel MRI Reconstruction: Impact on Subject and Group-Levels Statistical Sensitivity in fMRI
TL;DR: In this article, a 4D-UWR-SENSE algorithm was proposed to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. But the proposed 4D reconstruction scheme is fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan.
A review on graph optimization and algorithmic frameworks
Alessandro Benfenati,Emilie Chouzenoux,Laurent Duval,Jean-Christophe Pesquet,Aurelie Pirayre +4 more
TL;DR: This report presents a set of discrete optimization problems and resolution methods for edge selection problems and addresses the matrix optimization problems involved in the estimation of precision or covariance matrices given observations from multivariate Gaussian distribution.
Noise Covariance Properties in Dual-Tree
TL;DR: In this paper, the covariance properties of the dual-tree coefficients of a wide-sen se stationary process are derived in both one and two-dimensional cases, allowing to predict the behaviour of second-order moments for large lag values or at coarse resolution.
Proceedings ArticleDOI
Disparity map estimation under convex constraints using proximal algorithms
Mireille El Gheche,Jean-Christophe Pesquet,Joumana Farah,Caroline Chaux,Beatrice Pesquet-Popescu +4 more
TL;DR: This paper presents an iterative estimation method based on recent convex optimization algorithms and proximal tools that gives a great flexibility in the choice of the constrained criterion to be minimized, thus allowing us to take into account different types of noise distributions.