事件-光照协同的低光照图像增强与高分辨率真实世界数据集
Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset
事件相机与图像融合的低光照增强框架，其光照感知事件滤波（IAEF）和事件-光照协同交互（EICI）模块可复用于其他事件-图像融合任务；同时发布的首个高分辨率真实事件-图像配对数据集为事件视觉研究提供基准。
原文依据 we propose EIC-LIE, an event-illumination collaborative LIE framework
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于事件相机的低光照图像增强（LIE）方法主要关注引入事件的高动态范围（HDR）信息，而忽略了图像中本质的全局光照以及真实场景下事件信号固有的噪声敏感性。为解决这些问题，我们提出了EIC-LIE，一种事件-光照协同的低光照图像增强框架。具体而言，我们首先设计了一个事件-光照协同交互（EICI）模块，该模块包含两个关键过程：前向聚集，用于聚集不同光照条件下的HDR特征；以及反向注入，为光照和事件表示提供互补内容。接下来，我们引入了一个光照感知事件滤波器（IAEF），它基于从图像中导出的亮度统计量动态降低事件噪声。此外，我们构建了一个基于分束器的混合成像系统，从动态场景中采集时间同步的高质量事件-图像对，提供了首个高分辨率、真实世界的事件低光照图像增强数据集。大量实验表明，我们的EIC-LIE在五个真实世界和合成数据集上优于最优方法，显著超越先前方法，峰值信噪比（PSNR）提升高达1.24dB，SSIM提升0.069。代码和数据集已发布于https://github.com/QUEAHREN/EIC-LIE。
英文原文摘要 1361 字符
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.