Space-based visible camera is an important sensor for space situation
awareness during proximity operations. However, visible camera can be easily
affected by the low illumination in the space environment. Recently, deep
learning approaches have achieved remarkable success in image enhancement of
natural images datasets, but seldom applied in space due to the data
bottleneck. In this article, we propose a data-driven method for low-light
image enhancement (LLIE) of spin targets in space environment based on
diffusion model. Firstly, a dataset collection scheme is devised. To reduce the
domain gap and improve the diversity and quality of the dataset, we collect the
data with the camera on a ground-test system imitating the low lighting
conditions and relative attitude change of satellite in space. The satellite
motion is controlled by a 6-DoF robot. To generate different poses, a advanced
sampling method is combined with collision detection in physical simulation.
The entire process is automated. Based on our dataset, a novel diffusion model
is proposed. The diffusion and denoising process are directly conducted on the
grayscale channel to save computational resources. To take advantage of the
inner information of RGB channels, we rescale the RGB feature maps and insert
them into the downsampling layers to help feature extraction. The enhanced
results with our method have been verified to be better in image light
enhancement and competitive in image quality compared with previous methods. To
the best of our knowledge, this is the first work of LLIE using diffusion
model.

本文提出了一种基于扩散模型的飞行器低光图像增强方法，通过一种数据集收集方案和自动化的物理模拟生成器来对数据进行预处理。我们的方法在图像亮度增强方面表现更好，在图像质量方面与以前的方法相当。