  This paper introduces VESR-Net, a method for video enhancement and
super-resolution (VESR). We design a separate non-local module to explore the
relations among video frames and fuse video frames efficiently, and a channel
attention residual block to capture the relations among feature maps for video
frame reconstruction in VESR-Net. We conduct experiments to analyze the
effectiveness of these designs in VESR-Net, which demonstrates the advantages
of VESR-Net over previous state-of-the-art VESR methods. It is worth to mention
that among more than thousands of participants for Youku video enhancement and
super-resolution (Youku-VESR) challenge, our proposed VESR-Net beat other
competitive methods and ranked the first place.
