  The target of space-time video super-resolution (STVSR) is to increase the
spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR)
videos. Recent approaches based on deep learning have made significant
improvements, but most of them only use two adjacent frames, that is,
short-term features, to synthesize the missing frame embedding, which cannot
fully explore the information flow of consecutive input LR frames. In addition,
existing STVSR models hardly exploit the temporal contexts explicitly to assist
high-resolution (HR) frame reconstruction. To address these issues, in this
paper, we propose a deformable attention network called STDAN for STVSR. First,
we devise a long-short term feature interpolation (LSTFI) module, which is
capable of excavating abundant content from more neighboring input frames for
the interpolation process through a bidirectional RNN structure. Second, we put
forward a spatial-temporal deformable feature aggregation (STDFA) module, in
which spatial and temporal contexts in dynamic video frames are adaptively
captured and aggregated to enhance SR reconstruction. Experimental results on
several datasets demonstrate that our approach outperforms state-of-the-art
STVSR methods. The code is available at
https://github.com/littlewhitesea/STDAN.
