Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. To tackle these problems, we propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RGformer). To ensure detailed reconstruction and illumination correction, RLDM is empowered to acquire Retinex knowledge and extract reflectance and illumination priors. These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components. Following this, RGformer further enhances and consolidates the decomposed features, resulting in the production of refined images with consistent content and robustness to handle complex degradation scenarios. Extensive experiments show that Reti-Diff outperforms existing methods on three IDIR tasks, as well as downstream applications. Code will be available at \url{https://github.com/ChunmingHe/Reti-Diff}.

翻译：照度退化图像恢复技术旨在提升退化图像的可见性，并缓解不良照度带来的负面影响。在该类算法中，基于扩散模型的方法展现出良好性能，但常在预测图像级分布时面临计算负担沉重和像素对齐失准的问题。为解决上述挑战，我们提出在紧凑潜在空间中利用扩散模型生成简洁引导先验，并针对照度退化图像恢复任务引入名为Reti-Diff的新颖解决方案。Reti-Diff包含两个关键组件：基于Retinex的潜在扩散模型（RLDM）和Retinex引导的Transformer（RGformer）。为确保细节重建与照度校正，RLDM被赋予获取Retinex知识并提取反射率与照度先验的能力。这些先验随后由RGformer用于引导图像特征分解为对应的反射率分量和照度分量。在此基础上，RGformer进一步对分解特征进行增强与整合，最终生成具有内容一致性和鲁棒性以应对复杂退化场景的精修图像。大量实验表明，Reti-Diff在三个照度退化图像恢复任务及其下游应用中均优于现有方法。代码将发布于\url{https://github.com/ChunmingHe/Reti-Diff}。