DATA-VSR: Dynamic Trajectory Attention and Texture Adaptive Rooter for Video Super-Resolution

Abstract

Video Super-Resolution (VSR) is essential for reconstructing high-definition sequences from correlated video frames. While Transformer-based VSR methods have improved reconstruction quality, they require substantial computational resources, limiting deployment on resource-constrained devices. To tackle this issue, we propose a novel framework named Dynamic Trajectory Attention and Texture Adaptive Rooter for Video Super-Resolution (DATA-VSR). There are two key innovations: the Temporal Redundancy-aware Alignment Network (TRAN) and the Spatial Redundancy-aware Refinement Network (SRRN). Specifically, features are aligned by focusing on dynamic temporal trajectories instead of static redundancies in TRAN, and then features are adaptively refined based on the texture complexity of different regions in SRRN. Additionally, the Dual-Domain Enhancement Block (DDEB) is incorporated to effectively capture global dependencies in the frequency domain and enhance the representation of local features in the spatial domain. The experimental results on standard VSR benchmarks show that DATA-VSR achieves competitive performance with fewer parameters, lower FLOPs, and a specific reduction of 17%.