DP-Retinex: Dual-Prior Guided Low-Light Image Enhancement With YUV-Domain Reflectance-Illumination Decomposition

Accurate estimation of reflectance and illumination maps in the Retinex framework remains a significant challenge for low-light image enhancement due to inherent decomposition ambiguity. To address this, we propose DualPrior-Retinex, a novel framework that, inspired by Retinex theory, leverages the practical advantages of the YUV color space for robust enhancement. Our framework introduces a dual-prior architecture that effectively decouples the restoration process. It combines a diffusion-based global prior, responsible for ensuring low-frequency content consistency, with a YUV-based local prior designed to preserve high-frequency structural details. These complementary components are integrated by our Hierarchical Prior Fusion Module (HPFM), which balances perceptual quality with pixel-level fidelity in complex low-light scenarios. Extensive evaluations on multiple benchmarks demonstrate that our method achieves state-of-the-art performance across diverse metrics and visual qualities. Codes will be released at https://github.com/I2-Multimedia-Lab/DP-Retinex.