Progressive prompt-guided network for unsupervised low-light image enhancement

Abstract

Low-light images often suffer from severe noise, low contrast. Traditional low-light enhancement methods rely on paired data or fixed priors, limiting generalization in real world scenarios. To address these problems, we propose a Progressive Prompt-Guided Enhancement Network (PPGENet) for unsupervised low-light image enhancement. Unlike previous CLIP-based methods that rely on fixed or single-stage prompts, directly applying CLIP to low-light enhancement faces challenges in achieving progressive quality improvements. By leveraging multimodal priors from CLIP, our method learns adaptive prompts that progressively guide the enhancement process. We introduce a three-stage framework. First, the prompt warm-up stage initializes learnable prompts by performing cross-entropy classification on mixed low-light and normal light images, thereby establishing semantic anchors in CLIP space. Second, the unsupervised reconstruction stage trains a Unet enhancement network guided by the fixed prompts, incorporating multiple loss functions to restore brightness and suppress noise. Third, the prompt refinement stage progressively fine tunes the prompts using pseudo-labels derived from intermediate enhancements and dynamic margin ranking loss, thereby enforcing progressive semantic ordering. This framework alternates between prompt refinement and network training until convergence. Extensive experiments on multiple datasets demonstrate that PPGENet achieves superior performance in both quantitative metrics and visual quality.