Research map / Video / Video segmentation and tracking
Frame and Feature-Context Video Super-Resolution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use consecutive LR frames instead of only the current LR frame (ablation) | Local network for video SR · Vid4 average | PSNR: 24.971 -> 26.387 | Helped |
| Add context network using previously estimated HR frames (ablation) | Only local network · Vid4 average | PSNR: 26.387 -> 26.745 | Helped |
| Add local and context feature information (ablation) | FFCVSR without feature context · Vid4 average | PSNR: 26.745 -> 26.965 | Helped |
| Add optical flow while removing feature context (ablation) | FFCVSR without feature context · Vid4 average | PSNR: 26.745 -> 26.705 | Hurt |
| Refresh recurrent HR frame and feature with local-network outputs every T frames | FFCVSR inference · HongKong | reconstruction accuracy | Helped |
Closest papers by meaning
- Frame-Recurrent Video Super-Resolution (CVPR 2018 · similarity 0.93)
- Recurrent Back-Projection Network for Video Super-Resolution (CVPR 2019 · similarity 0.91)
- Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution (CVPR 2020 · similarity 0.91)
- SAVSR: Arbitrary-Scale Video Super-Resolution via a Learned Scale-Adaptive Network (AAAI 2024 · similarity 0.89)
- BF-STVSR: B-Splines and Fourier---Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution (CVPR 2025 · similarity 0.89)
- Event-Enhanced Blurry Video Super-Resolution (AAAI 2025 · similarity 0.88)
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