Zobrazeno 1 - 10
of 103
pro vyhledávání: '"RepettI, Audrey"'
Autor:
Bester, Hertzog L., Kenyon, Jonathan S., Repetti, Audrey, Perkins, Simon J., Smirnov, Oleg M., Blecher, Tariq, Mhiri, Yassine, Roth, Jakob, Heywood, Ian, Wiaux, Yves, Hugo, Benjamin V.
The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with da
Externí odkaz:
http://arxiv.org/abs/2412.10073
In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, in a Plug-and-Play (PnP) fashion. In particular, we consider both analy
Externí odkaz:
http://arxiv.org/abs/2411.13276
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a specific task by unrolling a proximal algorithm for a fixed number of
Externí odkaz:
http://arxiv.org/abs/2408.08742
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss. The proposed method is particularly effective in solving classes of variational problems, specifically monotone inclusion problem
Externí odkaz:
http://arxiv.org/abs/2404.00390
Autor:
Lauga, Guillaume, Repetti, Audrey, Riccietti, Elisa, Pustelnik, Nelly, Gonçalves, Paulo, Wiaux, Yves
This paper presents a multilevel algorithm specifically designed for radio-interferometric imaging in astronomy. The proposed algorithm is used to solve the uSARA (unconstrained Sparsity Averaging Reweighting Analysis) formulation of this image resto
Externí odkaz:
http://arxiv.org/abs/2403.13385
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 thiscontext, iterative proximal algorithms are widely used, enabling to h
Externí odkaz:
http://arxiv.org/abs/2308.03139
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical guarantees on the asymptotic behaviour of these methods remain scarce.
Externí odkaz:
http://arxiv.org/abs/2307.06987
Autor:
Garcia, Carlos Santos, Larchevêque, Mathilde, O'Sullivan, Solal, Van Waerebeke, Martin, Thomson, Robert R., Repetti, Audrey, Pesquet, Jean-Christophe
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., for microendoscopy). Recently, a single-pixel imaging technique bas
Externí odkaz:
http://arxiv.org/abs/2306.11679
Autor:
Tang, Michael, Repetti, Audrey
Uncertainty quantification in image restoration is a prominent challenge, mainly due to the high dimensionality of the encountered problems. Recently, a Bayesian uncertainty quantification by optimization (BUQO) has been proposed to formulate hypothe
Externí odkaz:
http://arxiv.org/abs/2304.11200
Stochastic differentiable approximation schemes are widely used for solving high dimensional problems. Most of existing methods satisfy some desirable properties, including conditional descent inequalities, and almost sure (a.s.) convergence guarante
Externí odkaz:
http://arxiv.org/abs/2302.06447