  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 convolutional denoising network in place of the proximal
operator of the prior in an alternating optimization scheme. We distinguish
ourselves from prior PnP work by directly applying that method to restore a
digital video from a degraded video observation. This way, a network trained
once for denoising can be repurposed for other video restoration tasks. Our
experiments in video deblurring, super-resolution, and interpolation of random
missing pixels all show a clear benefit to using a network specifically
designed for video denoising, as it yields better restoration performance and
better temporal stability than a single image network with similar denoising
performance using the same PnP formulation. Moreover, our method compares
favorably to applying a different state-of-the-art PnP scheme separately on
each frame of the sequence. This opens new perspectives in the field of video
restoration.
