Video Super-Resolution Based on Multi-Flow Guided Deformable Alignment and Temporal-Spatial Fusion

Video super resolution aims to recover high resolution frames from multiple low resolution ones. Compared to single frame super resolution, the main challenge of video super resolution is how to effectively utilize the spatiotemporal information in video sequences. Due to inaccurate alignment of information between frames, the reconstruction results often suffer from motion blur and artifacts, which will accumulate during the propagation process and seriously affect the reconstruction quality and continuity of the video. To solve this issue, this paper proposes a Multi-Flow Guided Deformable Alignment module(MFGDA) based on a bidirectional propagation network. This module combines the multi-optical flow information of neighboring frames with deformable convolution, accurately aligns features through bidirectional information propagation, and eliminates artifacts. In addition, to better utilize spatiotemporal information, we also design a Temporal-Spatial Feature Fusion Module (TSFM) based on an attention mechanism. This module mines the spatiotemporal information of features at different time steps, eliminates the alignment errors that occur during long-term information propagation, and enhances the fusion of key features. The experimental results demonstrate that our proposed method achieves superior performance compared with state-of-the-art methods.