Aug 2026· Applied Sciences· Vol 16, pp. 8329· 0 citations· 75 references
TL;DR
The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone, and uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions.
Abstract
Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone. Specifically, the denoising module uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions. We then guide the reverse sampling of the pre-trained diffusion model with a refinement strategy operating in both the frequency and spatial domains, so that illumination enhancement and local detail refinement can be jointly achieved during sampling. At each step, Fourier-based reconstruction contributes to illumination enhancement while preserving structural information, and illumination-guided spatial adjustment further refines local brightness. Experiments on multiple benchmark datasets show that the proposed method improves illumination while preserving structural details.
Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paired supervision or lack reliable scene constraints in zero-shot settings, often leading to structural inconsistency and color drift. Motivated by conventional Retinex models, which offer physically interpretable priors that can serve as reliable scene constraints yet struggle with mixed degradations in real-world scenarios, we propose DARD, a zero-shot Degradation-Aware Retinex-guided Diffusion framework for LLIE. DARD first extracts image-specific physical priors from the degraded input through a test-time degradation-aware Retinex decomposition, thereby providing reliable structural guidance for zero-shot restoration. It then injects these priors into reverse diffusion through a timestep-adaptive frequency fusion strategy to balance structural anchoring and detail generation. Finally, a guided reverse refinement process with physical consistency and Contrastive Language-Image Pre-training (CLIP)-based semantic guidance is introduced to suppress structural artifacts and semantic drift during sampling. Extensive experiments show that DARD achieves strong distortion and perceptual performance and consistently outperforms existing zero-shot baselines across multiple real-world low-light benchmarks. To further validate the practical utility of our method for downstream applications, we evaluated its impact on semantic segmentation. Experiments demonstrate that images enhanced by DARD achieve a 28.10% relative improvement in mIoU over AGLLDiff.
Wenjie Cai, Yuezhe Yang, Jian-Yang Xia 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
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.· Proceedings of the Thirty-Fi...· 0 citations
SFH-Net is proposed, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework that achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness.
Zhanqiang Huo, Hui-Jie Zhang, Yingxu Qiao et al.· Engineering Research Express· 0 citations
Low-light light field (L3F) images suffer from severe structural degradation, including low contrast, blurred edges, and heavy noise, which disrupts angular consistency. Existing single-image enhancement methods fail to exploit the spatial-angular consistency of light field (LF) images, while L3F enhancement methods struggle in low-light scenarios with extremely low contrast, often resulting in over-smoothed edges and the loss of geometric details. To address these issues, we propose the Edge-guided Hybrid Enhancement Network (EHENet), an efficient network integrating structural priors and spatial-angular extraction. We propose a structural prior embedding strategy that employs Scharr operators and Gaussian filtering to explicitly model spatial edges. Furthermore, we design the Global Feature Enhancement (GFE) block to extract spatial-angular correlation. Experiments show EHENet significantly outperforms state-of-the-art methods in both quality and efficiency.
Hao Wu, Bing-Jie Zhu, Shizheng Li et al.· International Conference on...· 0 citations
This work proposes a model-driven deep neural network to effectively handle the joint degradation of low light and blur and designs an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise.
Yao Xiao, You-Shen Xia, Zhen-Yu Lu et al.· IEEE Transactions on Neural...· 0 citations
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