  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}.
