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Found 5 papers, 4 papers with code
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems.
Video Restoration with a Deep Plug-and-Play Prior
Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme.
Deep Model-Based Super-Resolution with Non-uniform Blur
Instead, in this paper, we address the more realistic and computationally challenging case of spatially-varying blur.
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.
Ranked #5 on
Video Denoising
on Set8 sigma10
DVDnet: A Fast Network for Deep Video Denoising
Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods.
Ranked #5 on
Video Denoising
on DAVIS sigma30
