Video Super-Resolution Network with Gated High-Low Resolution Frames

Abstract

In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment in such scenes is the key to super-resolution reconstruction. In this paper, a new motion compensation method is proposed to design an alignment network based on gated high-low resolution frames. The core idea is to introduce a gating mechanism while using the information of high-low resolution neighboring frames to perform motion compensation adaptively. Meanwhile, within this alignment framework, we further introduce a pre-initial hidden state network and a local scale hierarchical salient feature fusion network. The pre-initial hidden state network is mainly used to reduce the impact of unbalanced quality effects between frames that occur in one-way cyclical networks; the local scale hierarchical salient feature fusion network is used to fuse the features of aligned video frames to extract contextual information and locally salient features to improve the reconstruction quality of the video. Compared with existing video super-resolution methods, this method achieves good performance and clearer edge and texture details.