Abstract
Performing video restoration in embedded systems is a challenge. Indeed, state-of-the-art learning-based methods in video restoration are specifically trained for given tasks, and often rely on huge networks, which makes them unsuitable when memory and computing resources are limited. In this paper, we explore the use of deep Plug-and-Play (PnP) algorithms for video restoration. We distinguish ourselves from prior PnP work by directly writing PnP schemes on video sequences instead of separate images. Our experiments in video deblurring, super-resolution (SR), and pixel interpolation all show a clear benefit to using a network specifically designed for video denoising, as it yields better restoration performance than a single image network with similar denoising performance using the same PnP formulation, and more temporal stability than other image-based PnP works. Experiments also highlight that, while not always as effective as state-of-the-art single-task deep networks, our lightweight approach remains competitive for many video restoration tasks.