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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Deep Neural Network Structures Solving Variational Inequalities
TL;DR: In this article, the authors investigate nonlinear composite models alternating proximity and affine operators defined on different spaces and establish conditions for the averagedness of the proposed composite constructs and investigate their asymptotic properties.
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Deep unfolding of a proximal interior point method for image restoration
Carla Bertocchi,Emilie Chouzenoux,Marie-Caroline Corbineau,Jean-Christophe Pesquet,Marco Prato +4 more
TL;DR: iRestNet is developed, a neural network architecture obtained by unfolding a proximal interior point algorithm that compares favorably with both state-of-the-art variational and machine learning methods in terms of image quality.
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Scalable splitting algorithms for big-data interferometric imaging in the SKA era
Alexandru Onose,Rafael E. Carrillo,Audrey Repetti,Jason D. McEwen,Jean-Philippe Thiran,Jean-Christophe Pesquet,Yves Wiaux +6 more
TL;DR: Two new convex optimisation algorithmic structures able to solve the conveX optimisation tasks arising in radio-interferometric imaging are proposed and employed in parallel and distributed computations to achieve scalability, in terms of memory and computational requirements.
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A nonlocal structure tensor-based approach for multicomponent image recovery problems
TL;DR: The results demonstrate the interest of introducing a nonlocal ST regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods, such as the alternating direction method of multipliers.
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Dual constrained TV-based regularization on graphs
TL;DR: This work extends the TV dual framework that includes Chambolle's and Gilboa and Osher's projection algorithms for TV minimization and uses a flexible graph data representation that allows it to generalize the constraint on the projection variable.