Bi-RSTU: Bidirectional Recurrent Upsampling Network for Space-Time Video Super-Resolution

Abstract

One-stage space-time video super-resolution (STVSR) aims to directly reconstruct high-resolution (HR) and high frame rate (HFR) video from its low-resolution (LR) and low frame rate (LFR) counterpart. Due to the wide application, one-stage STVSR has drawn much attention recently. However, existing one-stage methods suffer from ineffective exploration of the auxiliary information from adjacent time steps that may be useful to STVSR at the current time step. To address this issue, we propose a novel Bidirectional Recurrent Space-Time Upsampling network called Bi-RSTU for one-stage STVSR to utilize auxiliary information at various time steps. Specifically, an efficient channel attention feature interpolation (ECAFI) module is devised to synthesize the intermediate frame's LR feature by exploiting its two neighboring LR video frame features. Subsequently, we fuse the information from the previous time step into these intermediate and neighboring features. Finally, second-order attention spindle (SOAS) blocks are stacked to form the feature reconstruction module that learns a mapping from LR fused feature space to HR feature space. Experimental results on public datasets demonstrate that our Bi-RSTU shows competitive performance compared with current two-stage and one-stage state-of-the-art STVSR methods.