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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Journal ArticleDOI
Wavelet thresholding for some classes of non–Gaussian noise
TL;DR: This paper proposes a framework in which the notion of sparseness can be naturally expressed by a Bayesian model for the wavelet coefficients of the underlying signal and establishes close connections between wavelet thresholding techniques and Maximum A Posteriori estimation for two classes of noise distributions including heavy–tailed noises.
Journal ArticleDOI
A Majorize-Minimize Subspace Approach for $\ell_2-\ell_0$ Image Regularization
TL;DR: In this article, a nonconvex regularization is applied to an arbitrary linear transform of the target image, where edge-preserving measures or frame-analysis potentials commonly used in image processing are considered.
Proceedings ArticleDOI
A forward-backward view of some primal-dual optimization methods in image recovery
TL;DR: In this article, a general form of the forward-backward algorithm applied in a suitable product space is shown to provide efficient solutions to large-scale optimization problems for image recovery.
Journal ArticleDOI
M-band nonlinear subband decompositions with perfect reconstruction
TL;DR: This work considers a triangular representation of linear filterbanks and sees that it may be easily extended to the nonlinear case, and presents general nonlinear filterbanks, for which perfect reconstruction is either inherently guaranteed or ensured subject to an easily verified condition.
Journal ArticleDOI
Quadratic Higher Order Criteria for Iterative Blind Separation of a MIMO Convolutive Mixture of Sources
TL;DR: New contrast functions for blind signal separation that make use of reference signals are proposed, inspired by a semiblind approach, and appear as very appealing tools compared with some classical contrast functions.