  Single Image Super-Resolution (SISR) aims to recover a high-resolution image
from a given low-resolution version of it. Video Super Resolution (VSR) targets
series of given images, aiming to fuse them to create a higher resolution
outcome. Although SISR and VSR seem to have a lot in common, most SISR
algorithms do not have a simple and direct extension to VSR. VSR is considered
a more challenging inverse problem, mainly due to its reliance on a sub-pixel
accurate motion-estimation, which has no parallel in SISR. Another complication
is the dynamics of the video, often addressed by simply generating a single
frame instead of a complete output sequence.
  In this work we suggest a simple and robust super-resolution framework that
can be applied to single images and easily extended to video. Our work relies
on the observation that denoising of images and videos is well-managed and very
effectively treated by a variety of methods. We exploit the Plug-and-Play-Prior
framework and the Regularization-by-Denoising (RED) approach that extends it,
and show how to use such denoisers in order to handle the SISR and the VSR
problems using a unified formulation and framework. This way, we benefit from
the effectiveness and efficiency of existing image/video denoising algorithms,
while solving much more challenging problems. More specifically, harnessing the
VBM3D video denoiser, we obtain a strongly competitive motion-estimation free
VSR algorithm, showing tendency to a high-quality output and fast processing.
