248 citations · 771 across the 28 of their papers we have counts for
6 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…
A stochastic use of the Kurdyka-Lojasiewicz property: Investigation of optimization algorithms behaviours in a non-convex differentiable framework
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic differentiable approximation schemes are widely used for solving high dimensional problems. Most of existing methods satisfy some desirable properties, including conditi…
Uncertainty Quantification in CT pulmonary angiography
Adwaye M Rambojun, Hend Komber, Jennifer Rossdale +4
Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acqui…