248 citations · 253 across the 6 of their papers we have counts for
4 papers · 1 filter
Unfolded proximal neural networks for robust image Gaussian denoising
Hoang Trieu Vy Le, Audrey Repetti, Nelly Pustelnik
A common approach to solve inverse imaging problems relies on finding a maximum a posteriori (MAP) estimate of the original unknown image, by solving a minimization problem. In thi…
A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical g…
A primal-dual data-driven method for computational optical imaging with a photonic lantern
Carlos Santos Garcia, Mathilde Larchevêque, Solal O'Sullivan +4
Optical fibres aim to image in-vivo biological processes. In this context, high spatial resolution and stability to fibre movements are key to enable decision-making processes (e.g…
A Plug-and-Play Method with Inpainting Network for Bayesian Uncertainty Quantification in Imaging
Xiaoyu Wang, Michael Tang, Audrey Repetti
We contribute to an uncertainty quantification problem in imaging that evaluates a hypothesis test questioning the existence of local "artefacts" appearing in the maximum a posteri…