Deeply Feature Fused Video Super-resolution Network

Abstract

The video super-resolution (VSR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. An important step in VSR is to fuse the features of the reference frame with the features of the supporting frame. The existing VSR method does not make full use of the information provided by the distant neighboring frame, and usually fuses in a one-stage manner. In this paper, we propose a deep fusion video super-resolution network based on temporal grouping. We divide the input sequence into groups according to different frame rates to provide more accurate supplementary information, and the method aggregates temporal and spatial information at different stages of fusion.