SCI-Mamba：基于无监督学习的非合作航天器低光图像增强

中文摘要 AI 辅助

低光视觉感知是针对非合作航天器的在轨服务任务的核心视觉基础。星载图像存在严重的低光退化问题，且配对的正常/低光空间样本极度稀缺，限制了监督增强算法的泛化能力。本文提出SCI-Mamba，一种用于低光轨道航天器观测的无监督增强网络。该框架结合了自校准无监督学习、线性复杂度VMamba架构和Retinex物理先验，构建了Space Dark-1.0数据集。与其他方法的比较验证了SCI-Mamba在视觉真实性、色彩保真度和推理速度方面的优势，为近距离非合作空间操作提供了实用的低光增强解决方案。

英文摘要

Low-light visual perception acts as the core visual foundation for on-orbit servicing missions targeting non-cooperative spacecraft, supporting autonomous rendezvous, pose estimation, component detection and robotic capture operations. Spaceborne imagery suffers from severe low-light degradation, while the extreme scarcity of paired normal/low-light space samples severely limits the generalization capacity of supervised enhancement algorithms. To address this practical bottleneck, this paper proposes SCI-Mamba, an unsupervised enhancement network for low-light orbital spacecraft observations. The proposed framework unites self-calibrated unsupervised learning, linear-complexity VMamba architecture and Retinex physical priors, delivering a lightweight enhancement pipeline adaptable to resource-limited spaceborne hardware. We construct Space Dark-1.0, a dedicated low-light spacecraft dataset integrating real orbital footage, darkroom hardware-in-the-loop measurements and physically constrained synthetic data covering diverse illumination, motion and attitude conditions. Comprehensive comparisons with CNN-, Transformer- and prevailing Mamba-based approaches verify the advantages of SCI-Mamba in visual authenticity, color fidelity and inference speed. The proposed framework provides a practical low-light enhancement solution for close-proximity non-cooperative space operations. The code is available at https://github.com/bitswh/SCI-Mamba

发表机构

• School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学航天学院）

机构由 AI 辅助整理，请以论文原文为准。