Light the Moon: Asymmetric Physics-Guided Dual-Domain Network for Low-Light Enhancement of Lunar Shadowed Regions

Abstract

Low-light image enhancement of remote sensing imagery within lunar permanently shadowed regions (PSRs) is a critical prerequisite for ensuring safe landing and precise navigation for future crewed missions and geological resource prospecting. Given the absence of direct sunlight, images acquired in PSRs typically exhibit insufficient brightness, severe noise, and degradation of structural details, which are detrimental to lunar polar oriented exploration tasks. Furthermore, constrained by the lack of paired datasets, existing methods rely on unsupervised, single-degradation paradigms; their inability to accurately model complex lunar degradations within a unified framework leads to suboptimal results. To address these challenges, we propose an asymmetric physics-guided dual-domain network (APDNet), which decomposes the PSR enhancement task into illumination decoupling, illumination restoration, and structure restoration. The asymmetric architecture allows us to perform task-oriented enhancements for different modules according to their respective subtasks. Specifically, a frequency-domain processing mechanism is introduced to achieve spectral supervision for illumination restoration. Global and local latent information is processed in parallel and fused to restore degraded textures. In addition, the first paired low-light image dataset specifically dedicated to lunar PSR is constructed in this work. Through the modeling and learning of the degradation parameters of the real-world lunar environment, a PSR synthetic dataset is also constructed to facilitate supervised model training. Extensive experiments demonstrate that our proposed model achieves state-of-the-art (SOTA) enhancement performance on the PSR low-light dataset. Furthermore, evaluations on generic low-light datasets demonstrate competitive performance and strong generalization capabilities compared to existing SOTA methods, thoroughly validating the effectiveness of our approach.