A ground-based dataset and a diffusion model for on-orbit low-light image enhancement

TL;DR

This paper introduces a new dataset of space images and a diffusion model for enhancing low-light images captured in orbit, addressing the challenges of space environment conditions with deep learning.

Contribution

It presents a novel on-orbit low-light image dataset and a diffusion-based enhancement model with fused attention for better contrast and detail preservation.

Findings

The dataset reduces domain gap and improves diversity for space image enhancement.

The proposed diffusion model outperforms previous methods on the new dataset.

Enhanced images show better contrast and detail without over-exposure.

Abstract

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.