2026· IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing· Vol 19, pp. 25884-25899· 0 citations· 70 references
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.
Comprehensive comparisons with CNN-, Transformer- and prevailing Mamba-based approaches verify the advantages of SCI-Mamba in visual authenticity, color fidelity and inference speed, and provides a practical low-light enhancement solution for close-proximity non-cooperative space operations.
Yiyong Sun, W. Shan, Shijun Wei et al.· 0 citations
Low-light conditions degrade remote sensing imagery by reducing contrast, distorting color, and obscuring fine terrain structures and small objects critical for Earth observation. Accurate illumination adjustment under spatially varying scene content remains challenging for existing enhancement methods, and many prior-guided approaches operate exclusively in either the spatial domain or the frequency domain. In this work, we propose a Dual-Domain Illumination Prior (DDIP), a trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics. DDIP comprises three components: a Frequency-Domain Illumination Distribution Prior (FIDP) that performs per-color-channel amplitude calibration in Fourier space to improve global brightness; a Spatial-Domain Illumination Distribution Prior (SIDP), adapted from IDP-Net, that performs multi-scale sub-region statistical correction for local illumination adjustment; and a Selective Core Feature Fusion (SCFF) module that adaptively combines the frequency-domain output, the spatial-domain output, and the original input through an attention-based gating mechanism with dual pooling. DDIP is integrated with each host backbone while leaving its main restoration blocks unchanged. In the controlled reconstruction comparisons on iSAID-dark and the evaluated general low-light benchmarks, equipping the tested backbone networks with DDIP improves PSNR and SSIM over their corresponding baselines. Complementary LPIPS and CIELAB lightness measurements characterize perceptual similarity and lightness behavior, while a fixed-detector object-detection evaluation on the tested high-resolution iSAID-dark scenes examines the effect of the enhancement pipelines under the reported synthetic low-light conditions. The ablation studies further examine the contribution of the module components within the reported experimental settings.
Chao Wang, Zhe Pan, Liangtian He et al.· Remote Sensing· 0 citations
Nighttime driving and adverse weather expose persistent weaknesses in autonomous-driving perception pipelines. Low illumination, fog, rain, snow, glare, wet-road reflections, and motion blur degrade camera, LiDAR, radar, and event-camera inputs in modality-specific ways. This review examines robust perception under low-visibility conditions as a pipeline-level problem requiring joint consideration of image enhancement, sensor fusion, detection, and evaluation. Rather than treating enhancement as an isolated restoration task, it analyzes whether recent methods preserve detector-relevant structures, exploit cross-sensor complementarity, and improve downstream 2D and 3D perception. A taxonomy-driven narrative approach compares representative studies along five axes, from input modality and supervision strategy to evaluation protocol and deployment feasibility, supported by a structured verification search of literature published between January 2020 and July 2026, with the search strategy, eligibility criteria, and corpus composition documented. Recent work indicates a shift from image-quality-oriented restoration toward perception-driven optimization, in which enhancement and fusion modules are evaluated by their effect on object detection and 3D perception. Remaining challenges include generalization to compound degradations, cross-sensor misalignment, scene-dependent sensor reliability, latency on in-vehicle edge platforms, and inconsistent benchmark protocols. Future systems should therefore jointly model degradation severity, sensor reliability, downstream task performance, and real-time constraints rather than optimizing restoration, fusion, and detection modules in isolation.
Abstract. Integration of LROC NAC and ShadowCam imagery is essential for meter-scale controlled mapping of the entire lunar south pole including Permanently Shadowed Regions (PSRs), but remains challenging due to extreme radiometric differences, sparse overlap across illumination boundaries, and ill-conditioned bundle adjustment networks. This paper proposes a LOLA DEM-mediated multi-source bundle adjustment framework for controlled lunar polar mapping. A hierarchical cross-modality matching strategy is developed using first- and second-order Gaussian steerable gradient features with multi-scale fusion and phase-correlation-based subpixel refinement. Sensor-specific geometric models are established using second-order polynomial transformations for NAC orthoimages and rational polynomial models for ShadowCam map-projected images. Five types of geometric constraints are formulated to integrate intra-sensor, limited cross-sensor, and image-to-DEM observations, with the LOLA DEM acting as a common geometric mediator. To stabilize the heterogeneous network, a hybrid L1-L2 regularization model with adaptive two-stage weighting is optimized using ADMM algorithm. Experiments in the lunar south polar region demonstrate substantial improvements on intra-sensor, cross-sensor, and image-to-reference positioning accuracy. The final seamless 1 m/pixel orthorectified mosaics achieve approximately 5 m absolute accuracy, validating the proposed framework for geometrically unifying illuminated and permanently shadowed terrain in lunar polar controlled mapping.
Qionghua You, Zhen Ye, Yusheng Xu et al.· The International Archives o...· 0 citations
Low-light image enhancement (LLIE) is a fundamental task in computer vision, required for restoring luminance, contrast, and structural fidelity in images captured under suboptimal lighting environments. This paper introduces an optimization-driven, scene-adaptive LLIE framework, designated as OMIDCPGCGL, which exploits the optical duality between low-light inversion and atmospheric scattering. The proposed methodology transforms low-light inputs into quasi-haze representations through an optical inversion process, followed by structural restoration using an Improved Dark Channel Prior (MIDCP) baseline. To refine the restored output, a Gamma Correction with Global Limits (GCGL) module is integrated as a boundary constraint to mitigate localized over-exposure and preserve chromatic consistency. A core novelty of this framework lies in the deployment of metaheuristic optimization algorithms (MOAs)—specifically the Gray Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Marine Predators Algorithm (MPA)—to autonomously resolve optimal, image-specific parameter configurations. This search paradigm is guided by perception-driven fitness functions, namely the Patch-based Contrast Quality Index (PCQI) or the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Quantitative and qualitative evaluations across a comprehensive pool of 1092 benchmark images demonstrate that the proposed framework exhibits robust statistical resilience and cross-dataset generalization compared to four state-of-the-art deep learning methods. While data-driven deep learning architectures retain localized superiority under the extreme degradation boundaries of the DARK FACE dataset, the proposed physics-inspired optimization framework achieves the leading overall cross-dataset aggregate ranking (R¯=2.467) across diverse evaluation environments due to its per-image dynamic solution space mapping.