127 citations · 145 across the 5 of their papers we have counts for
3 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…
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…