Papers › Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video...
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
Xingyu Zhou, Wei Long, Jingbo Lu, Shiyin Jiang, Weiyi You, Haifeng Wu, Shuhang Gu
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Super-Resolution | REDS4- 4x upscaling | LRTI-VSR | PSNR | 33.06 | #1 of 7 | Archive leaderboard | report |
| Video Super-Resolution | REDS4- 4x upscaling | LRTI-VSR | SSIM | 0.9162 | #1 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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