无需成对数据，用Mamba结构实现太空低光图像增强
SCI-Mamba: Unsupervised Learning based Low-Light Image Enhancement for Non-Cooperative Spacecraft
- 结合自校准无监督学习与Retinex物理先验，轻量化设计适配航天硬件
- 在自建的Space Dark-1.0数据集上显著提升视觉真实感和色彩保真度
- 适合资源受限的近轨非合作航天器视觉任务，代码已开源
低光视觉是轨道服务任务中对非合作航天器进行自主交会、位姿估计、部件检测与机械臂捕获的核心基础。星载影像存在严重低光退化，而真实正常/低光成对样本极度稀缺，制约了监督增强算法的泛化能力。为此，本文提出SCI-Mamba，一种面向轨道航天器观测的无监督增强网络。该框架融合自校准无监督学习、线性复杂度的VMamba架构与Retinex物理先验，构建轻量级增强流水线，适配资源受限的星载硬件。我们构建了Space Dark-1.0数据集，包含真实轨道视频、暗室硬件闭环测量及物理约束合成数据，覆盖多样光照、运动与姿态条件。与基于CNN、Transformer及主流Mamba的方法相比，SCI-Mamba在视觉真实性、色彩保真度和推理速度上均表现更优。该框架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。代码已公开于https://github.com/bitswh/SCI-Mamba。
原文摘要 · Abstract (English)
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
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