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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Proceedings ArticleDOI
Source separation by quadratic contrast functions: a blind approach based on any higher-order statistics
TL;DR: It is shown that this approach is applicable not only in a semi-blind context, but also in a completely blind scenario, and the appeal of this new class of contrast functions.
Proceedings ArticleDOI
Adaptive lifting for multicomponent image coding through quadtree partitioning
TL;DR: Simulations performed on real satellite images show that the proposed adaptive method outperforms the conventional non-adaptive lifting schemes.
Journal ArticleDOI
Some results on the wavelet packet decomposition of nonstationary processes
TL;DR: This paper investigates in a general framework, the existence and some properties of the cumulants of wavelet packet coefficients, and investigates more precisely the almost-cyclostationary case, and determines the asymptotic distributions of wave let packet coefficients.
Journal ArticleDOI
Projection-based rank reduction algorithms for multichannel modelling and image compression
TL;DR: It is shown that problems of spectral estimation and enhancement via global rank reduction techniques may be solved by the new design algorithm proposed here, which also provides a decomposition in terms of the eigencomponents of the multichannel signal.
Journal ArticleDOI
A Spatial Regularization Approach for Vector Quantization
TL;DR: This paper investigates vector quantization combined with regularity constraints, a little-studied area of interest, and shows that when using a small number of levels, this approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.