Abstract: On-orbit service is important for maintaining the sustainability of space environment. A space-based visible camera is an economical and lightweight sensor for situational awareness during on-orbit service. However, it can be easily affected by the low illumination environment. Recently, deep learning (DL) has achieved remarkable success in image enhancement of natural images, but is 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 focused on reducing the domain gap and improving the diversity of the dataset. We collected 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. Then, a novel diffusion model is proposed. To enhance the image contrast without over-exposure and blurred details, we designed fused attention guidance (FAG) to highlight the structure and dark region. Finally, a comparison of our method with previous methods indicates that our method has better on-orbit LLIE performance.