Abstract
Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradations. Learning-based approaches offer fast inference but still struggle with heavy noise in real low-light scenes, primarily due to limitations in effectively leveraging temporal information. In this paper, we address this issue with DWTA-Net, a novel two-stage framework that jointly exploits short- and long-term temporal cues. Stage I employs Visual State-Space blocks for multi-frame alignment, recovering brightness, color, and structure with local consistency. Stage II introduces a recurrent refinement module with dynamic weight-based temporal aggregation guided by optical flow, adaptively balancing static and dynamic regions. A texture-adaptive loss further preserves fine details while promoting smoothness in flat areas. Experiments on real-world low-light videos show that DWTA-Net effectively suppresses noise and artifacts, delivering superior visual quality compared with state-of-the-art methods.
Abstract (translated)
低光视频增强（LLVE）由于噪声、对比度低和颜色退化的问题而具有挑战性。基于学习的方法提供了快速的推理，但仍然难以处理真实低光场景中的严重噪声问题，主要原因是无法有效利用时间信息。在本文中，我们通过引入DWTA-Net框架来解决这一问题，这是一个新颖的两阶段框架，能够同时利用短时和长时的时间线索。 第一阶段使用视觉状态空间块进行多帧对齐，恢复亮度、颜色和结构，并保持局部一致性。 第二阶段则引入了一个基于光学流引导动态权重的时间聚合递归细化模块，根据场景自适应地平衡静态区域和平移区域的处理。此外，一个纹理自适应损失被用于保留细节的同时在平坦区域促进平滑度。 实验结果表明，在真实世界的低光视频上，DWTA-Net能够有效地抑制噪声和伪影，并且视觉质量明显优于最先进的方法。
URL
https://arxiv.org/abs/2510.09450
