Retinex-guided generative diffusion prior for low-light image enhancement

Abstract

Existing Retinex-based training-free low-light image enhancement (LLIE) methods often rely on complex architectures or lack support for text-controlled personalization. In this paper, we propose RetinexGDP, a training-free and text-controllable LLIE framework that uniquely integrates Retinex-based image modeling with generative diffusion priors. First, we introduce a simplified Retinex decomposition by embedding weighted total variation optimization into a single Gaussian convolutional layer, enabling robust illumination estimation without the need for training. Next, we guide the diffusion denoising process using the estimated reflectance map, employing patch-wise inversion and reflectance-conditioned sampling to effectively suppress noise while preserving structural details. Finally, unlike previous diffusion-based LLIE methods that perform only monotonous global brightness enhancement, we incorporate text guidance into the sampling process, enabling controllable enhancement that aligns with user-specific stylistic preferences. RetinexGDP thus provides a modular, interpretable, and text-controllable solution for low-light image enhancement. Experimental results show that RetinexGDP achieves state-of-the-art performance in terms of NIQMC and CPCQI metrics across seven real-world datasets. Code will be available at: https://github.com/zhaozunjin/PLIE