127 citations · 145 across the 5 of their papers we have counts for
5 papers
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…
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…
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
Matias Tassano, Julie Delon, Thomas Veit
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks ha…
DVDnet: A Fast Network for Deep Video Denoising
Matias Tassano, Julie Delon, Thomas Veit
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoi…