  Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates
high-quality videos with higher resolution and higher frame rates. Existing
advanced methods accomplish ST-VSR tasks through the association of Spatial and
Temporal video super-resolution (S-VSR and T-VSR). These methods require two
alignments and fusions in S-VSR and T-VSR, which is obviously redundant and
fails to sufficiently explore the information flow of consecutive spatial LR
frames. Although bidirectional learning (future-to-past and past-to-future) was
introduced to cover all input frames, the direct fusion of final predictions
fails to sufficiently exploit intrinsic correlations of bidirectional motion
learning and spatial information from all frames. We propose an effective yet
efficient recurrent network with bidirectional interaction for ST-VSR, where
only one alignment and fusion is needed. Specifically, it first performs
backward inference from future to past, and then follows forward inference to
super-resolve intermediate frames. The backward and forward inferences are
assigned to learn structures and details to simplify the learning task with
joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to
aggregate and distill information to refine spatial information and reconstruct
high-quality video frames. Extensive experiments on two public datasets
demonstrate that our method outperforms state-of-the-art methods in efficiency,
and reduces calculation cost by about 22%.
