  Video super-resolution (VSR) aims at restoring a video in low-resolution (LR)
and improving it to higher-resolution (HR). Due to the characteristics of video
tasks, it is very important that motion information among frames should be well
concerned, summarized and utilized for guidance in a VSR algorithm. Especially,
when a video contains large motion, conventional methods easily bring
incoherent results or artifacts. In this paper, we propose a novel deep neural
network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for
super-resolution of videos with large motion. We design a new module named
U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit
motion estimation and motion compensation (MEMC) as well as coarse spatial
feature extraction. And we present a new Multi-Stage Communicated Upsampling
(MSCU) module to make full use of the intermediate results of upsampling for
guiding the VSR. Moreover, a novel dual subnet is devised to aid the training
of our DSMC, whose dual loss helps to reduce the solution space as well as
enhance the generalization ability. Our experimental results confirm that our
method achieves superior performance on videos with large motion compared to
state-of-the-art methods.
