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CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution

Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate high-quality 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. 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation; 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 (CycMuNet) 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 spatial and temporal features are fully fused and distilled, helping high-quality video reconstruction. In addition, we also show interesting extensions for efficient network design (CycMuNet+), such as parameter sharing and dense connection on projection units and feedback mechanism in CycMuNet. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMuNet (+) with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms the state-of-the-art methods.

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