Due to the complexity and diversity of practical environments, real-world image dehazing remains an unresolved problem, with one of the key challenges being how to bridge the distribution gap between synthetic and real domains. This paper proposes a Prompt-driven Domain Adaptation (PDDA) framework within the bi-level optimization perspective. Specifically, we introduce hyperparameter optimization-based bi-level modeling: the lower-level optimization emphasizes prior learning within the synthetic domain to stabilize dehazing performance, while the upper-level optimization focuses on enhancing cross-domain adaptability to ensure that the model can generalize across different domains. Given the scarcity of paired real haze images, we train learnable haze prompts by jointly optimizing the text-image similarity between positive/negative prompts and corresponding clear/haze images in the CLIP latent space to more effectively capture real-world haze characteristics. Based on the learned haze prompts, we construct an unsupervised cross-domain loss function that enhances the adaptability to complex real-world scenarios by integrating prompt learning with bi-level optimization strategy. Furthermore, we conduct a comprehensive exploration to uncover the inherent properties of PDDA, including architecture-irrelevant flexibility and domain-agnostic robustness. Extensive experiments across a wide range of benchmark datasets demonstrate that our method achieves both quantitative and qualitative improvements across diverse scenarios, showing robust performance not only in real-world daytime conditions but also exhibiting superior cross-domain adaptation capabilities in nighttime scenarios. Codes are available at https://github.com/YanZhang-zy/PDDA.git.
Zhang et al. (2026) studied this question.