  Video super-resolution, which aims at producing a high-resolution video from
its corresponding low-resolution version, has recently drawn increasing
attention. In this work, we propose a novel method that can effectively
incorporate temporal information in a hierarchical way. The input sequence is
divided into several groups, with each one corresponding to a kind of frame
rate. These groups provide complementary information to recover missing details
in the reference frame, which is further integrated with an attention module
and a deep intra-group fusion module. In addition, a fast spatial alignment is
proposed to handle videos with large motion. Extensive results demonstrate the
capability of the proposed model in handling videos with various motion. It
achieves favorable performance against state-of-the-art methods on several
benchmark datasets.
