Abstract
Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments. Prevalent event-based solutions primarily utilize events triggered by motion, i.e., ''motion events'' to strengthen only the edge texture, while leaving the high dynamic range and excellent low-light responsiveness of event cameras largely unexplored. This paper instead opens a new avenue from the perspective of estimating the illumination using ''temporal-mapping'' events, i.e., by converting the timestamps of events triggered by a transmittance modulation into brightness values. The resulting fine-grained illumination cues facilitate a more effective decomposition and enhancement of the reflectance component in low-light images through the proposed Illumination-aided Reflectance Enhancement module. Furthermore, the degradation model of temporal-mapping events under low-light condition is investigated for realistic training data synthesizing. To address the lack of datasets under this regime, we construct a beam-splitter setup and collect EvLowLight dataset that includes images, temporal-mapping events, and motion events. Extensive experiments across 5 synthetic datasets and our real-world EvLowLight dataset substantiate that the devised pipeline, dubbed RetinEV, excels in producing well-illuminated, high dynamic range images, outperforming previous state-of-the-art event-based methods by up to 6.62 dB, while maintaining an efficient inference speed of 35.6 frame-per-second on a 640X480 image.
Abstract (translated)
低光图像增强（LLIE）旨在改善在光线不足环境中捕获的图像的可见度。目前流行的事件驱动解决方案主要利用由运动触发的事件，即“运动事件”，来强化边缘纹理，而未充分利用事件相机高动态范围和卓越的低光响应性能。 本文则从通过“时间映射”事件估算照明的角度开辟了一条新途径，即将透射率调制触发的事件的时间戳转换为亮度值。由此产生的精细光照线索有助于更有效地分解并增强低光图像中的反射分量，通过提出辅助光照增强模块实现这一点。此外，还研究了在低光条件下时间映射事件退化模型，以进行现实训练数据合成。为了弥补此类数据集的不足，我们构建了一个分束器装置，并收集了包含图像、时间映射事件和运动事件的EvLowLight数据集。 跨5个合成数据集以及我们的真实世界EvLowLight数据集进行了广泛的实验，证实所设计的管道——称为RetinEV，在生成光照良好且动态范围高的图像方面表现出色，优于之前的最先进的事件驱动方法高达6.62 dB，并在分辨率为640x480的图像上保持每秒35.6帧的有效推理速度。
URL
https://arxiv.org/abs/2504.09379
