Abstract
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}\%.
Abstract (translated)
低光图像增强旨在恢复在黑暗场景中拍摄的曝光不足的图像。在这种情况下，传统的帧基摄像头由于受限于曝光时间而可能无法捕捉到结构和颜色信息。事件相机是一种仿生视觉传感器，能异步响应像素亮度变化，并具有高动态范围，在极端低光照条件下对于视觉感知至关重要，超过了传统相机的表现能力，从而能够应用于挑战性的黑暗环境。 在本文中，受Retinex理论成功用于传统帧基低光图像恢复的启发，我们首次将Retinex理论与事件相机结合，并提出了一种名为ERetinex的新框架。我们的贡献之一是开发了一种新方法，利用来自事件相机的高时间分辨率数据和传统的图像信息来准确估计场景照明。这种方法在低光照环境下超越了传统仅基于图像的技术，提供了更精确的照明信息。 此外，我们还提出了一种有效的融合策略，将事件相机提供的高动态范围数据与传统图像的颜色信息相结合以增强图像质量。通过这种融合方法，我们可以生成更为清晰和细节丰富的图像，在极端光线条件下也能保持视觉信息的完整性。 实验结果表明，我们的方法超越了现有最先进的（SOTA）技术，在PSNR上提高了1.0613分，同时将FLOPS减少了84.28%。
URL
https://arxiv.org/abs/2503.02484
