A Relative Illumination Structure Estimation (RISE) framework is proposed that decouples relative illumination structure from absolute exposure and infers it from reliable bright regions, enabling interpretable and robust enhancement.
Abstract
Existing unsupervised low-light image enhancement (LLIE) methods often estimate illumination directly from the entire low-light input, without separating its spatially varying illumination pattern, termed relative illumination structure, from the absolute exposure level or preventing unreliable low signal-to-noise ratio regions from biasing the estimate. Moreover, fixed exposure targets impose a scene-agnostic enhancement criterion, limiting adaptation across diverse lighting conditions. Inspired by the spatial propagation of light, we propose a Relative Illumination Structure Estimation (RISE) framework that decouples relative illumination structure from absolute exposure and infers it from reliable bright regions, enabling interpretable and robust enhancement. For scene-adaptive exposure adjustment, we further propose a Dual-Metering Exposure Reference derived from each input, allowing RISE to adapt the enhancement strength to individual scenes and generalize across diverse lighting conditions. Extensive benchmark and real-world generalization experiments show that RISE achieves state-of-the-art performance among unsupervised LLIE methods while producing visually natural results.
SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise and a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement.
Yue Zhang, Zhi-Liang Wu, Yuxuan Hou et al.· 0 citations
Low-light image enhancement is commonly formulated as an illumination recovery problem. However, severe illumination degradation not only reduces image brightness but also weakens structural responses, causing edge discontinuities and unstable texture reconstruction. To address these challenges, low-light enhancement is reconsidered as a structure-constrained reconstruction problem, where illumination recovery and structural continuity preservation are jointly optimized. Although supervised methods can achieve promising enhancement quality, their reliance on paired low-light and normal-light images limits their applicability to diverse real-world scenarios. Unsupervised approaches provide a more flexible solution by avoiding the requirement for paired training data. In this work, a continuity-guided low-light enhancement network, termed CGLEN, is proposed for unsupervised structure-aware image reconstruction. CGLEN introduces a learnable Retinex decomposition module to estimate illumination and reflectance components, followed by a gradient-guided structural representation that provides reliable structural cues during enhancement. Furthermore, a PDE-inspired structural continuity refinement strategy is developed by incorporating gradient variation and Laplacian consistency into a lightweight residual propagation framework, enabling spatial continuity preservation under degraded illumination conditions. A structure-guided modulation mechanism, together with an auxiliary reconstruction branch and adaptive fusion strategy, is further introduced to improve optimization stability and reconstruction consistency. Extensive experiments on multiple benchmark datasets demonstrate that CGLEN achieves competitive enhancement performance compared with existing supervised and unsupervised methods, while maintaining relatively low computational complexity. The results indicate that explicitly modeling structural continuity provides an effective strategy for unsupervised low-light image enhancement, particularly in challenging illumination conditions.
Yang Li, Ruobo Xu, Kai Zhou· Scientific Reports· 0 citations
Existing methods typically require training a separate model for each dataset, making them difficult to generalize across diverse illumination conditions. To address this limitation, we propose a novel low-light image enhancement method based on a Mixture of Experts (MoE) mechanism with fast adaptation. In our framework, the MoE gating network adaptively fuses the outputs of multiple experts to handle different lighting conditions, while only the expert and gating networks are fine-tuned when adapting to new datasets, significantly improving training efficiency and generalization. Each expert is designed as a multi-task module that jointly performs color correction and noise reduction, thereby enhancing both visual fidelity and robustness. Extensive quantitative and qualitative experiments demonstrate that the proposed method not only surpasses state-of-the-art approaches in noise reduction and color preservation, but also rapidly adapts to new illumination distributions with fast training across multiple benchmark datasets with significantly reduced fine-tuning cost and training time.
Yi Wang, Haonan Su, Zhaolin Xiao· IEEE Signal Processing Lette...· 0 citations
A trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics, which improves PSNR and SSIM over their corresponding baselines.
Chao Wang, Zhe Pan, Liangtian He et al.· Remote Sensing· 0 citations
Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.
Tianqi Jiang· Poster Volume 0007 The 2026...· 0 citations
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