Abstract: On-orbit service is important for maintaining the sustainability of the 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 has achieved remarkable success in image enhancement of natural images, but it is seldom applied in space due to the data bottleneck. In this study, we first propose a dataset of BeiDou navigation satellites for on-orbit low-light image enhancement (LLIE). In the automatic data collection scheme, we focus on reducing the 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 orientations and distances without collision, we propose a collision-free workspace and pose-stratified sampling. Subsequently, we develop a novel diffusion model. To enhance the image contrast without over-exposure and blurred details, we design fused attention guidance to highlight the structure and the dark region. Finally, a comparison of our method with previous methods indicates that our method has better on-orbit LLIE performance.

Key words: Satellite capture; Low-light image enhancement (LLIE); Data collection; Diffusion model; Fused attention

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