Geometry-aware Depth-guided Representation Learning for Structure-preserving Low-light Image Enhancement

• URL: http://arxiv.org/abs/2607.05005v1
• Date: Mon, 06 Jul 2026 12:46:15 GMT
• Title: Geometry-aware Depth-guided Representation Learning for Structure-preserving Low-light Image Enhancement
• Abstract summary: Low-light degradation weakens structural cues that are important for visual representation and scene understanding.<n>Existing low-light image enhancement methods mainly focus on appearance restoration.<n>This paper proposes a Depth-guided Multi-scale Attention Network (DMSA-Net) for geometry-aware low-light image enhancement.
• Score: 21.407073477296553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light degradation reduces image visibility and weakens structural cues that are important for visual representation and scene understanding. Existing low-light image enhancement methods mainly focus on appearance restoration, while insufficiently exploiting scene geometry to preserve structural consistency. To address this limitation, this paper proposes a Depth-guided Multi-scale Attention Network (DMSA-Net) for geometry-aware low-light image enhancement. DMSA-Net introduces depth-related structural priors into low-light representation learning through reflectance-geometry interaction. A Retinex-based decomposition module is first used to obtain illumination-invariant reflectance representations, from which depth cues are inferred to characterize scene structure under degraded illumination. A multi-scale depth-guided fusion strategy is then embedded into a hierarchical encoder-decoder architecture, where depth-aware attention adaptively integrates geometric and appearance features. Experiments on several benchmark datasets show that DMSA-Net achieves effective low-light restoration while improving structural preservation. Moreover, we construct LOL-D, a depth-augmented low-light dataset, to facilitate research on geometry-aware low-light vision.

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