{"ID":5937108,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T12:40:20.160756429Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05005","arxiv_id":"2607.05005","title":"Geometry-aware Depth-guided Representation Learning for Structure-preserving Low-light Image Enhancement","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.","short_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 addr...","url_abs":"https://arxiv.org/abs/2607.05005","url_pdf":"https://arxiv.org/pdf/2607.05005v1","authors":"[\"Fang Gao\",\"Jiongkai Qin\",\"Jiabao Wang\",\"Jingfeng Tang\",\"Ming Cheng\",\"Hanbo Zheng\",\"Qingbao Huang\",\"Cheng Wu\"]","published":"2026-07-06T12:46:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}