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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Vector-lifting schemes for lossless coding and progressive archival of multispectral images
TL;DR: A nonlinear subband decomposition scheme with perfect reconstruction is proposed for lossless and progressive coding of multispectral images to exploit efficiently the spatial and the spectral redundancies contained in the multisectral images related to a scene of interest.
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Vector Lifting Schemes for Stereo Image Coding
TL;DR: A novel approach, based on vector lifting schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views is proposed, which indicates a significant improvement using the proposed structures compared with conventional methods.
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A hierarchical Bayesian model for frame representation
Lotfi Chaari,Jean-Christophe Pesquet,Jean-Yves Tourneret,Philippe Ciuciu,Amel Benazza-Benyahia +4 more
TL;DR: A hierarchical Bayesian model for frame representation is introduced and the posterior distribution of the frame coefficients and model hyperparameters is derived and Hybrid Markov chain Monte Carlo algorithms are proposed to sample from this posterior distribution.
Posted Content
Stochastic Approximations and Perturbations in Forward-Backward Splitting for Monotone Operators
TL;DR: In this paper, the authors investigate the asymptotic behavior of a stochastic version of the forward-backward splitting algorithm for finding a zero of the sum of a maximally monotone set-valued operator and a cocoercive operator in Hilbert spaces.
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A family of frequency- and time-domain contrasts for blind separation of convolutive mixtures of temporally dependent signals
TL;DR: This paper addresses the problem of blind separation of convolutive mixtures via contrast maximization using Parseval's formula and new frequency domain contrast functions are constructed based on higher order spectra of the observations.