  Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate
super-resolved videos with higher resolution(HR) and higher frame rate (HFR).
Quite intuitively, pioneering two-stage based methods complete ST-VSR by
directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and
Temporal Video Super-Resolution(T-VSR) but ignore the reciprocal relations
among them. Specifically, 1) T-VSR to S-VSR: temporal correlations help
accurate spatial detail representation with more clues; 2) S-VSR to T-VSR:
abundant spatial information contributes to the refinement of temporal
prediction. To this end, we propose a one-stage based Cycle-projected Mutual
learning network (CycMu-Net) for ST-VSR, which makes full use of
spatial-temporal correlations via the mutual learning between S-VSR and T-VSR.
Specifically, we propose to exploit the mutual information among them via
iterative up-and-down projections, where the spatial and temporal features are
fully fused and distilled, helping the high-quality video reconstruction.
Besides extensive experiments on benchmark datasets, we also compare our
proposed CycMu-Net with S-VSR and T-VSR tasks, demonstrating that our method
significantly outperforms state-of-the-art methods.
