  Video super-resolution, which attempts to reconstruct high-resolution video
frames from their corresponding low-resolution versions, has received
increasingly more attention in recent years. Most existing approaches opt to
use deformable convolution to temporally align neighboring frames and apply
traditional spatial attention mechanism (convolution based) to enhance
reconstructed features. However, such spatial-only strategies cannot fully
utilize temporal dependency among video frames. In this paper, we propose a
novel deep learning based VSR algorithm, named Deformable Kernel Spatial
Attention Network (DKSAN). Thanks to newly designed Deformable Kernel
Convolution Alignment (DKC_Align) and Deformable Kernel Spatial Attention
(DKSA) modules, DKSAN can better exploit both spatial and temporal redundancies
to facilitate the information propagation across different layers. We have
tested DKSAN on AIM2020 Video Extreme Super-Resolution Challenge to
super-resolve videos with a scale factor as large as 16. Experimental results
demonstrate that our proposed DKSAN can achieve both better subjective and
objective performance compared with the existing state-of-the-art EDVR on
Vid3oC and IntVID datasets.
