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
Building robust wavelet estimators for multicomponent images using Stein's principle
TL;DR: The application of Stein's principle is applied to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise in order to exploit the correlations existing between the different spectral components.
Journal ArticleDOI
A SURE Approach for Digital Signal/Image Deconvolution Problems
TL;DR: The restoration problem is formulated as a nonlinear estimation problem leading to the minimization of a criterion derived from Stein's unbiased quadratic risk estimate and the deconvolution procedure is performed using any analysis and synthesis frames that can be overcomplete or not.
Journal ArticleDOI
Epigraphical projection and proximal tools for solving constrained convex optimization problems
TL;DR: In this article, a proximal approach is proposed to deal with a class of convex variational problems involving nonlinear constraints, which can be expressed as the lower-level set of a sum of a convex functions evaluated over different blocks of the linearly transformed signal.
Journal ArticleDOI
A Convex Approach for Image Restoration with Exact Poisson--Gaussian Likelihood
TL;DR: This work proposes a convex optimization strategy for the reconstruction of images degraded by a linear operator and corrupted with a mixed Poisson-Gaussian noise, and shows that in a variational framework the Shifted Poisson and Exponential approximations lead to very good restoration results.
Journal ArticleDOI
Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.
Nathalie Lassau,Samy Ammari,Emilie Chouzenoux,Hugo Gortais,Paul Herent,Matthieu Devilder,Samer Soliman,Olivier Meyrignac,Marie-Pauline Talabard,Jean-Philippe Lamarque,Remy Dubois,Nicolas Loiseau,Paul Trichelair,Etienne Bendjebbar,Gabriel Garcia,Corinne Balleyguier,Mansouria Merad,Annabelle Stoclin,Simon Jégou,Franck Griscelli,Nicolas Tetelboum,Yingping Li,Sagar Verma,Matthieu Terris,Tasnim Dardouri,Kavya Gupta,Ana Neacsu,Frank Chemouni,Meriem Sefta,Paul Jehanno,Imad Bousaid,Yannick Boursin,Emmanuel Planchet,Mikael Azoulay,Jocelyn Dachary,Fabien Brulport,Adrian Gonzalez,Olivier Dehaene,Jean-Baptiste Schiratti,Kathryn Schutte,Jean-Christophe Pesquet,Hugues Talbot,Elodie Pronier,Gilles Wainrib,Thomas Clozel,Fabrice Barlesi,Marie-France Bellin,Michael G. B. Blum +47 more
TL;DR: In this paper, a deep learning model based on CT scans was used to predict severity of the SARS-COV-2 pandemic in intensive care units (ICU).