Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras' high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by \textbf{84.28}\%.

翻译：低光照图像增强旨在恢复在暗光场景下拍摄的曝光不足图像。在此类场景中，受曝光时间限制，传统的基于帧的相机可能无法捕捉到结构和颜色信息。事件相机是一种受生物启发的视觉传感器，能够异步响应像素级的亮度变化。事件相机的高动态范围对于极端低光照场景下的视觉感知至关重要，它超越了传统相机，使得在具有挑战性的暗光环境中应用成为可能。本文受Retinex理论在传统基于帧的低光照图像恢复中成功的启发，首次提出了将Retinex理论与事件相机相结合的方法，并提出了一个新颖的基于Retinex的低光照图像恢复框架，命名为ERetinex。我们的贡献主要包括：首先，开发了一种新方法，该方法利用事件相机的高时间分辨率数据与传统图像信息来精确估计场景光照。与仅使用图像的传统技术相比，此方法通过提供更精确的光照信息，在低光照环境下表现更优。此外，我们提出了一种有效的融合策略，将事件相机的高动态范围数据与传统图像的颜色信息相结合，以提升图像质量。通过这种融合，我们能够生成更清晰、细节更丰富的图像，即使在极端光照条件下也能保持视觉信息的完整性。实验结果表明，我们提出的方法优于当前最先进（SOTA）的方法，在PSNR上获得了1.0613 dB的提升，同时将FLOPS降低了\textbf{84.28}\%。