Video super-resolution (VSR) can achieve better performance compared to single image super-resolution by additionally leveraging temporal information. In particular, the recurrent-based VSR model exploits long-range temporal information during inference and achieves superior detail restoration. However, effectively learning these long-term dependencies within long videos remains a key challenge. To address this, we propose LRTI-VSR, a novel training framework for recurrent VSR that efficiently leverages Long-Range Refocused Temporal Information. Our framework includes a generic training strategy that utilizes temporal propagation features from long video clips while training on shorter video clips. Additionally, we introduce a refocused intra&inter-frame transformer block which allows the VSR model to selectively prioritize useful temporal information through its attention module while further improving inter-frame information utilization in the FFN module. We evaluate LRTI-VSR on both CNN and transformer-based VSR architectures, conducting extensive ablation studies to validate the contribution of each component. Experiments on long-video test sets demonstrate that LRTI-VSR achieves state-of-the-art performance while maintaining training and computational efficiency.
View on arXiv@article{zhou2025_2505.02159,
title={ Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution },
author={ Xingyu Zhou and Wei Long and Jingbo Lu and Shiyin Jiang and Weiyi You and Haifeng Wu and Shuhang Gu },
journal={arXiv preprint arXiv:2505.02159},
year={ 2025 }
}