  The target of space-time video super-resolution (STVSR) is to increase both
the frame rate (also referred to as the temporal resolution) and the spatial
resolution of a given video. Recent approaches solve STVSR using end-to-end
deep neural networks. A popular solution is to first increase the frame rate of
the video; then perform feature refinement among different frame features; and
last increase the spatial resolutions of these features. The temporal
correlation among features of different frames is carefully exploited in this
process. The spatial correlation among features of different (spatial)
resolutions, despite being also very important, is however not emphasized. In
this paper, we propose a spatial-temporal feature interaction network to
enhance STVSR by exploiting both spatial and temporal correlations among
features of different frames and spatial resolutions. Specifically, the
spatial-temporal frame interpolation module is introduced to interpolate low-
and high-resolution intermediate frame features simultaneously and
interactively. The spatial-temporal local and global refinement modules are
respectively deployed afterwards to exploit the spatial-temporal correlation
among different features for their refinement. Finally, a novel motion
consistency loss is employed to enhance the motion continuity among
reconstructed frames. We conduct experiments on three standard benchmarks,
Vid4, Vimeo-90K and Adobe240, and the results demonstrate that our method
improves the state of the art methods by a considerable margin. Our codes will
be available at
https://github.com/yuezijie/STINet-Space-time-Video-Super-resolution.
