针对极低光视频噪声问题，提出动态加权时序聚合方法，提升增强效果。
Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise
- 采用两阶段递归架构，结合多帧对齐与动态加权时序聚合
- 在真实低光数据集上噪声抑制更强，视觉质量优于现有方法
- 适合处理高噪声低光视频，尤其对运动场景有良好适应性
低光视频增强（LLVE）因噪声、对比度低和色彩失真而具有挑战性。基于学习的方法虽推理快速，但在强现实噪声下常表现不佳，因其未能充分挖掘长期时序线索。本文提出DWTA-Net，一种新型深度递归式低光视频增强框架。该框架采用集成式两阶段设计：第一阶段通过多帧对齐，利用Mamba模型实现时序一致的局部结构与色彩恢复；第二阶段采用由光流引导的动态权重时序聚合机制，实现递归优化，作为自适应运动感知去噪器。此外，提出纹理自适应损失函数，在纹理区域保留细节，同时在均质区域抑制噪声。在真实低光视频数据上的实验表明，相比当前最优方法，DWTA-Net展现出更强的噪声抑制能力与更少伪影，显著提升视觉质量。
原文摘要 · Abstract (English)
Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design. DWTA-Net adopts an integrated two-stage architecture: Stage I restores local structure and color via multi-frame alignment for temporally consistent Mamba-based enhancement, while Stage II performs recurrent refinement using a novel dynamic weight-based temporal aggregation guided by optical flow, functioning as a recurrent denoiser that adapts to motion. We further introduce a texture-adaptive loss that preserves fine details in textured regions while suppressing noise in homogeneous areas. Experiments on real-world low-light footage show that DWTA-Net achieves stronger noise suppression and fewer artifacts, delivering superior visual quality compared with state-of-the-art methods.
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