  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.
