轻量级视频修复模型，专注关键信息传递，效果媲美大模型
LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation
- 只传递与退化无关的特征，避免干扰时序建模
- 仅362K参数，参数量不足现有模型1%
- 在多个基准上表现相当甚至更优，适合部署
近期研究探索了统一处理多种视频退化的全功能模型。然而，面对时变退化时，现有方法仍面临两大挑战：其一，退化特征会主导时序建模，使模型关注伪影而非内容；其二，当前方法多依赖大型模型，掩盖了根本问题。为此，我们提出轻量级全功能视频修复网络 LaverNet，仅含362K参数。为减轻退化对时序建模的影响，我们设计了一种新型选择性传播机制，仅跨帧传递与退化无关的特征。通过 LaverNet，我们证明小型网络亦可实现强大修复性能。尽管参数量不足现有模型的1%，其在多个基准上仍达到相当甚至更优的表现。
原文摘要 · Abstract (English)
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.
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