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

Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning