Stereo Video Super-Resolution via Exploiting View-Temporal Correlations

Abstract

Stereo Video Super-Resolution (StereoVSR) aims to generate high-resolution video steams from two low-resolution videos under stereo settings. Existing video super-resolution and stereo image super-resolution techniques can be extended to tackle the StereoVSR task, yet they cannot make full use of the multi-view and temporal information to achieve satisfactory performance. In this paper, we propose a novel Stereo Video Super-Resolution Network (SVSRNet) to fulfill the StereoVSR task via exploiting view-temporal correlations. First, we devise a view-temporal attention module (VTAM) to integrate the information of cross-time-cross-view for constructing high-resolution stereo videos. Second, we propose a spatial-temporal fusion module (STFM), which aggregates the information across time in intra-view to emphasize important features for subsequent restoration. In addition, we design a view-temporal consistency loss function to enforce consistency constraint of superresolved stereo videos. Comprehensive experimental results demonstrate that our method generates superior results.