Retinex-Based Self-Conditioned Diffusion Model for Low-Light Image Enhancement

Abstract

The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional priors to guide the enhancement process, often resulting in unsatisfactory results. To address the issue, we propose Retinex-Based Self-Conditioned Diffusion Models, dubbed RSCDM, which utilizes self-conditioned illumination representation learning and representation guidance enhancement to generate high-quality image. To be specific, in the first stage, we pretrain a retinex decomposed model (RDM) to capture illumination representation and devise a illumination-representation restoration model (IRM) to accurately reconstruct the representation from noisy images. Moreover, we further design dynamic resblock (DRB) and dynamic simplified attention gated block (DSAGB) as basic units of IRM for better fine-grained restoration. In the second stage, we employ a self-conditioned diffusion model (SDM) to generate realistic results conditioned on the illumination representation. Extensive experiments demonstrates our method outperforms the existing SOTA methods both quantitatively and qualitatively. The codes will be publicly available.