On-orbit service is important for maintaining the sustainability of space environment. Space-based visible camera is an economical and lightweight sensor for situation awareness during on-orbit service. However, it can be easily affected by the low illumination environment. Recently, deep learning has achieved remarkable success in image enhancement of natural images, but seldom applied in space due to the data bottleneck. In this article, we first propose a dataset of the Beidou Navigation Satellite for on-orbit low-light image enhancement (LLIE). In the automatic data collection scheme, we focus on reducing domain gap and improving the diversity of the dataset. we collect hardware in-the-loop images based on a robotic simulation testbed imitating space lighting conditions. To evenly sample poses of different orientation and distance without collision, a collision-free working space and pose stratified sampling is proposed. Afterwards, a novel diffusion model is proposed. To enhance the image contrast without over-exposure and blurring details, we design a fused attention to highlight the structure and dark region. Finally, we compare our method with previous methods using our dataset, which indicates that our method has a better capacity in on-orbit LLIE.

翻译：在轨服务对于维持太空环境的可持续性至关重要。基于天基可见光的相机是在轨服务期间进行态势感知的经济且轻量化的传感器，然而它极易受到低照度环境的影响。近年来，深度学习在自然图像增强领域取得了显著成功，但由于数据瓶颈，其在太空领域的应用仍十分有限。本文首次提出面向北斗导航卫星在轨低光照图像增强（LLIE）的数据集。在自动数据采集方案中，我们聚焦于缩小域差异并提升数据集的多样性。通过模拟太空光照条件的机器人仿真测试平台，我们采集了硬件在环图像。为实现不同方位与距离的均匀位姿采样且避免碰撞，提出了一种无碰撞工作空间与位姿分层采样方法。随后，我们提出了一种新型扩散模型。为在增强图像对比度的同时避免过度曝光与细节模糊，我们设计了一种融合注意力机制，以突出结构信息与暗区特征。最后，基于所构建数据集与现有方法的对比实验表明，本方法在在轨低光照图像增强任务中具有更优性能。