融合事件与光照信息，提升真实场景低光图像增强效果
Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset
- 设计事件-光照协同模块，双向融合高低光特征
- 动态抑制事件噪声，提升真实场景鲁棒性，最高增益1.24dB PSNR
- 构建首个高分辨率同步事件-图像数据集，适合低光视觉研究
基于事件的低光图像增强方法通常依赖事件信号提供的高动态范围信息，却忽视了图像中的全局光照特性以及事件信号在真实场景下的固有噪声敏感性。为此，我们提出EIC-LIE框架，通过事件-光照协同交互模块（EICI），实现正向汇聚不同光照条件下的高动态范围特征，并反向注入补充光照与事件表征内容；进一步引入光照感知事件滤波器（IAEF），根据图像亮度统计动态抑制事件噪声。此外，我们搭建基于分束器的混合成像系统，采集具有时间同步性的高质量事件-图像对，构建首个高分辨率真实世界事件基低光增强数据集。大量实验表明，所提EIC-LIE在五个真实与合成数据集上均优于现有方法，最大提升达1.24dB PSNR和0.069 SSIM。代码与数据集已开源。
原文摘要 · Abstract (English)
Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumination in images and the inherent noise sensitivity of event signals in real-world scenarios. To address these issues, we propose EIC-LIE, an event-illumination collaborative LIE framework. Concretely, we first design an Event-Illumination Collaborative Interaction (EICI) module, which contains two key processes: forward gathering, which gathers HDR features across varying lighting conditions, and backward injection, which provides complementary content for illumination and event representations. Next, we introduce an Illumination-aware Event Filter (IAEF) that dynamically reduces event noise based on brightness statistics derived from images. Additionally, we build a beam-splitter-based hybrid imaging system to collect high-quality event-image pairs with temporal synchronization from dynamic scenes, providing the first high-resolution, real-world event-based LIE dataset. Extensive experiments show that our EIC-LIE outperforms state-of-the-art methods on five real-world and synthetic datasets, significantly surpassing previous methods with improvements of up to 1.24dB in PSNR and 0.069 in SSIM. The code and dataset are released at https://github.com/QUEAHREN/EIC-LIE.
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