  Video super-resolution (VSR) is the task of restoring high-resolution frames
from a sequence of low-resolution inputs. Different from single image
super-resolution, VSR can utilize frames' temporal information to reconstruct
results with more details. Recently, with the rapid development of convolution
neural networks (CNN), the VSR task has drawn increasing attention and many
CNN-based methods have achieved remarkable results. However, only a few VSR
approaches can be applied to real-world mobile devices due to the computational
resources and runtime limitations. In this paper, we propose a \textit{Sliding
Window based Recurrent Network} (SWRN) which can be real-time inference while
still achieving superior performance. Specifically, we notice that video frames
should have both spatial and temporal relations that can help to recover
details, and the key point is how to extract and aggregate information. Address
it, we input three neighboring frames and utilize a hidden state to recurrently
store and update the important temporal information. Our experiment on REDS
dataset shows that the proposed method can be well adapted to mobile devices
and produce visually pleasant results.
