用事件流引导的二值化网络，提升低光视频增强效率与质量。
EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion
- 通过二值化神经网络压缩计算开销，保留细节。
- 融合RAW图像与事件流，实现时空信息互补增强。
- 适合移动端或嵌入式设备部署，兼顾速度与画质。
在资源受限场景下，极端低光条件下的视频增强仍面临恢复质量与计算效率难以平衡的挑战。本文提出EeveeDark，一种结合传感器级原始数据空间丰富性与事件流时间精确性的低光视频增强框架。核心采用二值化神经网络（BNN）架构，通过量化权重和激活值降低计算开销，同时保持细节。模型包含三部分：针对原始帧与事件数据的模态专用二值编码器、轻量级融合模块以整合时空线索，以及事件引导的跳跃门机制，实现动态时空优化。在合成与真实世界数据集上的实验表明，EeveeDark优于现有基于BNN的方法，并在性能-效率权衡上超越全精度模型。项目页面见https://cyberiada.github.io/EeveeDark。
原文摘要 · Abstract (English)
Enhancing videos under extreme low-light conditions remains challenging due to the difficulty of balancing restoration quality and computational efficiency in resource-constrained settings. This paper introduces EeveeDark, a low-light video enhancement framework that combines the spatial richness of sensor-level RAW data with the temporal precision of event streams. Central to our model is a Binary Neural Network (BNN) architecture that reduces computational overhead by quantizing weights and activations while preserving detail. EeveeDark incorporates (i) modality-specific binary encoders for processing RAW frames and event data, (ii) a lightweight fusion block for integrating spatial and temporal cues, and (iii) an event-guided skip gating mechanism for dynamic spatiotemporal refinement. Experiments on synthetic and real-world datasets show that EeveeDark outperforms prior BNN-based methods and offers a favorable performance-efficiency trade-off compared to full-precision models. The project page is available at https://cyberiada.github.io/EeveeDark.
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