  Video super-resolution (VSR) aims to reconstruct a sequence of
high-resolution (HR) images from their corresponding low-resolution (LR)
versions. Traditionally, solving a VSR problem has been based on iterative
algorithms that can exploit prior knowledge on image formation and assumptions
on the motion. However, these classical methods struggle at incorporating
complex statistics from natural images. Furthermore, VSR has recently benefited
from the improvement brought by deep learning (DL) algorithms. These techniques
can efficiently learn spatial patterns from large collections of images. Yet,
they fail to incorporate some knowledge about the image formation model, which
limits their flexibility. Unrolled optimization algorithms, developed for
inverse problems resolution, allow to include prior information into deep
learning architectures. They have been used mainly for single image restoration
tasks. Adapting an unrolled neural network structure can bring the following
benefits. First, this may increase performance of the super-resolution task.
Then, this gives neural networks better interpretability. Finally, this allows
flexibility in learning a single model to nonblindly deal with multiple
degradations. In this paper, we propose a new VSR neural network based on
unrolled optimization techniques and discuss its performance.
