Deep Learning in Lunar Regolith Image Processing: A Review

Recent lunar missions have generated a growing demand for automated and reliable processing of lunar regolith images. However, imaging degradations, limited annotations, and the lack of paired clean–degraded reference data still hinder the robustness and transferability of existing methods. Unlike previous studies focusing primarily on high-level geological interpretation, this review emphasizes the foundational role of low-level restoration and reconstruction in lunar regolith analysis. We organize recent progress into a full-pipeline framework spanning image reconstruction, morphology extraction, and geological interpretation, while clarifying the evidential roles of regolith-specific, lunar-surface-transferable, and general computer-vision references. Specifically, we review physically guided restoration, particle segmentation, and three-dimensional morphological quantification for irregular regolith grains, with particular attention to dense packing, occlusion, boundary ambiguity, and limited global-context reasoning in CNN-based segmentation. We further discuss downstream applications including mineralogical inversion, space-weathering characterization, multimodal fusion, and cross-modal collaborative representation linking microscopic regolith characterization with macroscopic orbital and in situ observations. The review also identifies degradation-induced error propagation across the pipeline, where unresolved low-level degradations may bias boundary delineation, morphological statistics, and downstream compositional and geological interpretation. We conclude that physically constrained benchmarks, joint restoration-analysis models, and lightweight transferable vision models are critical for improving scientific fidelity in data-limited and resource-constrained lunar exploration.