  Video super-resolution (VSR) methods have recently achieved a remarkable
success due to the development of deep convolutional neural networks (CNN).
Current state-of-the-art CNN methods usually treat the VSR problem as a large
number of separate multi-frame super-resolution tasks, at which a batch of low
resolution (LR) frames is utilized to generate a single high resolution (HR)
frame, and running a slide window to select LR frames over the entire video
would obtain a series of HR frames. However, duo to the complex temporal
dependency between frames, with the number of LR input frames increase, the
performance of the reconstructed HR frames become worse. The reason is in that
these methods lack the ability to model complex temporal dependencies and hard
to give an accurate motion estimation and compensation for VSR process. Which
makes the performance degrade drastically when the motion in frames is complex.
In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but
effective block, which can efficiently capture the motion compensation and feed
it back to the network in an adaptive way. Our approach efficiently utilizes
the information of the inter-frame motion, the dependence of the network on
motion estimation and compensation method can be avoid. In addition, benefiting
from the excellent nature of MAFC, the network can achieve better performance
in the case of extremely complex motion scenarios. Extensive evaluations and
comparisons validate the strengths of our approach, and the experimental
results demonstrated that the proposed framework is outperform the
state-of-the-art methods.
