LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908719967633408 |
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| author | Zhao, Haiyu Shan, Yiwen Gou, Yuanbiao Peng, Xi |
| author_facet | Zhao, Haiyu Shan, Yiwen Gou, Yuanbiao Peng, Xi |
| contents | Recent studies have explored all-in-one video restoration, which handles multiple degradations with a unified model. However, these approaches still face two challenges when dealing with time-varying degradations. First, the degradation can dominate temporal modeling, confusing the model to focus on artifacts rather than the video content. Second, current methods typically rely on large models to handle all-in-one restoration, concealing those underlying difficulties. To address these challenges, we propose a lightweight all-in-one video restoration network, LaverNet, with only 362K parameters. To mitigate the impact of degradations on temporal modeling, we introduce a novel propagation mechanism that selectively transmits only degradation-agnostic features across frames. Through LaverNet, we demonstrate that strong all-in-one restoration can be achieved with a compact network. Despite its small size, less than 1\% of the parameters of existing models, LaverNet achieves comparable, even superior performance across benchmarks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_16313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation Zhao, Haiyu Shan, Yiwen Gou, Yuanbiao Peng, Xi Computer Vision and Pattern Recognition Recent studies have explored all-in-one video restoration, which handles multiple degradations with a unified model. However, these approaches still face two challenges when dealing with time-varying degradations. First, the degradation can dominate temporal modeling, confusing the model to focus on artifacts rather than the video content. Second, current methods typically rely on large models to handle all-in-one restoration, concealing those underlying difficulties. To address these challenges, we propose a lightweight all-in-one video restoration network, LaverNet, with only 362K parameters. To mitigate the impact of degradations on temporal modeling, we introduce a novel propagation mechanism that selectively transmits only degradation-agnostic features across frames. Through LaverNet, we demonstrate that strong all-in-one restoration can be achieved with a compact network. Despite its small size, less than 1\% of the parameters of existing models, LaverNet achieves comparable, even superior performance across benchmarks. |
| title | LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16313 |