{"ID":6266995,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-12T01:02:22.86131488Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08033","arxiv_id":"2607.08033","title":"SCI-Mamba: Unsupervised Learning based Low-Light Image Enhancement for Non-Cooperative Spacecraft","abstract":"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","short_abstract":"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 scar...","url_abs":"https://arxiv.org/abs/2607.08033","url_pdf":"https://arxiv.org/pdf/2607.08033v1","authors":"[\"Yiyong Sun\",\"Weihang Shan\",\"Shijun Wei\",\"Diwei Zhou\",\"Guang Zhai\"]","published":"2026-07-09T01:17:44Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false,"code_links":[{"ID":614067,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-10T01:11:38.759438437Z","DeletedAt":null,"paper_id":6266995,"paper_url":"https://arxiv.org/abs/2607.08033","paper_title":"SCI-Mamba: Unsupervised Learning based Low-Light Image Enhancement for Non-Cooperative Spacecraft","repo_url":"https://github.com/bitswh/SCI-Mamba","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}