Abstract
Low-light image enhancement (LLIE) is vital for safety-critical applications such as surveillance, autonomous navigation, and medical imaging, where visibility degradation can impair downstream task performance. Recently, diffusion models have emerged as a promising generative paradigm for LLIE due to their capacity to model complex image distributions via iterative denoising. This survey provides an up-to-date critical analysis of diffusion models for LLIE, distinctively featuring an in-depth comparative performance evaluation against Generative Adversarial Network and Transformer-based state-of-the-art methods, a thorough examination of practical deployment challenges, and a forward-looking perspective on the role of emerging paradigms like foundation models. We propose a multi-perspective taxonomy encompassing six categories: Intrinsic Decomposition, Spectral & Latent, Accelerated, Guided, Multimodal, and Autonomous; that map enhancement methods across physical priors, conditioning schemes, and computational efficiency. Our taxonomy is grounded in a hybrid view of both the model mechanism and the conditioning signals. We evaluate qualitative failure modes, benchmark inconsistencies, and trade-offs between interpretability, generalization, and inference efficiency. We also discuss real-world deployment constraints (e.g., memory, energy use) and ethical considerations. This survey aims to guide the next generation of diffusion-based LLIE research by highlighting trends and surfacing open research questions, including novel conditioning, real-time adaptation, and the potential of foundation models.
Abstract (translated)
低光图像增强（LLIE）对于安全关键型应用如监控、自主导航和医学成像至关重要，因为在这些领域中，能见度下降会损害下游任务的性能。最近，扩散模型作为LLIE的一个有前景的生成范例而兴起，这得益于它们通过迭代去噪来建模复杂图像分布的能力。本综述提供了一种对扩散模型在LLIE中的最新批判性分析，特别强调了与生成对抗网络和基于Transformer的方法进行深入性能比较、详尽的实际部署挑战评估以及对未来新兴范式（如基础模型）作用的前瞻性视角。 我们提出一个多角度分类法，涵盖六个类别：内在分解、频谱及潜在空间、加速、指导、多模态及自主性；这些类别跨越物理先验知识、条件方案和计算效率来映射增强方法。我们的分类法基于对模型机制与条件信号两者混合视图的理解。 我们评估了定性的失败模式、基准测试的一致性和解释性、泛化能力以及推理效率之间的权衡。此外，我们也讨论了实际部署的约束（如内存、能耗）和伦理问题。 本综述旨在通过强调趋势并揭示开放的研究问题来指导下一代基于扩散模型的LLIE研究，这些问题包括新颖的条件设置、实时适应及基础模型的应用潜力。
URL
https://arxiv.org/abs/2510.05976
