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Spherical videos, also known as \ang{360} (panorama) videos, can be viewedwith various virtual reality devices such as computers and head-mounteddisplays. They attract large amount of interest since awesome immersion can beexperienced when watching spherical videos. However, capturing, storing andtransmitting high-resolution spherical videos are extremely expensive. In thispaper, we propose a novel single frame and multi-frame joint network (SMFN) forrecovering high-resolution spherical videos from low-resolution inputs. To takeadvantage of pixel-level inter-frame consistency, deformable convolutions areused to eliminate the motion difference between feature maps of the targetframe and its neighboring frames. A mixed attention mechanism is devised toenhance the feature representation capability. The dual learning strategy isexerted to constrain the space of solution so that a better solution can befound. A novel loss function based on the weighted mean square error isproposed to emphasize on the super-resolution of the equatorial regions. Thisis the first attempt to settle the super-resolution of spherical videos, and wecollect a novel dataset from the Internet, MiG Panorama Video, which includes204 videos. Experimental results on 4 representative video clips demonstratethe efficacy of the proposed method. The dataset and code are available athttps://github.com/lovepiano/SMFN_For_360VSR.

A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution