283 citations · 551 across the 22 of their papers we have counts for
6 papers · 1 filter
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
Charles Laroche, Andrés Almansa, Eva Coupeté +1
Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelit…
Video Restoration with a Deep Plug-and-Play Prior
Antoine Monod, Julie Delon, Matias Tassano +1
This paper presents a novel method for restoring digital videos via a Deep Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists in using a deep convolution…
Diverse super-resolution with pretrained deep hiererarchical VAEs
Jean Prost, Antoine Houdard, Andrés Almansa +1
We investigate the problem of producing diverse solutions to an image super-resolution problem. From a probabilistic perspective, this can be done by sampling from the posterior di…
Deep Model-Based Super-Resolution with Non-uniform Blur
Charles Laroche, Andrés Almansa, Matias Tassano
We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsamp…
Patch-Based Stochastic Attention for Image Editing
Nicolas Cherel, Andrés Almansa, Yann Gousseau +1
Attention mechanisms have become of crucial importance in deep learning in recent years. These non-local operations, which are similar to traditional patch-based methods in image p…
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Rémi Laumont, Valentin de Bortoli, Andrés Almansa +3
Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the…