  Most Video Super-Resolution (VSR) methods enhance a video reference frame by
aligning its neighboring frames and mining information on these frames.
Recently, deformable alignment has drawn extensive attention in VSR community
for its remarkable performance, which can adaptively align neighboring frames
with the reference one. However, we experimentally find that deformable
alignment methods still suffer from fast motion due to locally loss-driven
offset prediction and lack explicit motion constraints. Hence, we propose a
Matching-based Flow Estimation (MFE) module to conduct global semantic feature
matching and estimate optical flow as coarse offset for each location. And a
Flow-guided Deformable Module (FDM) is proposed to integrate optical flow into
deformable convolution. The FDM uses the optical flow to warp the neighboring
frames at first. And then, the warped neighboring frames and the reference one
are used to predict a set of fine offsets for each coarse offset. In general,
we propose an end-to-end deep network called Flow-guided Deformable Alignment
Network (FDAN), which reaches the state-of-the-art performance on two benchmark
datasets while is still competitive in computation and memory consumption.
