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
Wavelet synthesis by alternating projections
TL;DR: An alternating projection method is proposed to solve the constrained optimization problem of the coefficients of the quadrature mirror filters involved in orthonormal wavelet or wavelet packets signal decompositions.
Journal ArticleDOI
Dual Block-Coordinate Forward-Backward Algorithm with Application to Deconvolution and Deinterlacing of Video Sequences
Feriel Abboud,Emilie Chouzenoux,Emilie Chouzenoux,Jean-Christophe Pesquet,Jean-Hugues Chenot,Louis Laborelli +5 more
TL;DR: This work proposes new dual forward-backward formulations for computing the proximity operator of a sum of convex functions involving linear operators and introduces a block-coordinate strategy combined with a preconditioning technique.
Journal ArticleDOI
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
TL;DR: This paper extends 2D regularization in the wavelet domain to 3D-wavelet representations and the 3D sparsity-promoting regularization term, in order to address reconstruction artifacts that propagate across adjacent slices, and outperforms the SENSE reconstruction at the subject and group levels.
Proceedings ArticleDOI
Solving inverse problems with overcomplete transforms and convex optimization techniques
TL;DR: How the choice of the transform may influence parameters and operators necessary to implement algorithms is discussed, including convex optimization approaches that consist of minimizing a criteria generally composed of two terms: a data fidelity term and a prior term.
Book ChapterDOI
Best Basis Representations with Prior Statistical Models
TL;DR: This chapter proposes several techniques to derive the prior parameters and develops a Bayesian-based approach to the best basis problem and introduces prior models on the underlying signal in noise.