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.

Existing learning-based dehazing methods perform well on synthetic data but struggle in real scenarios due to the domain gap, causing residual haze and detail loss. To address this, we propose a Multilevel Subspace Distribution Adapter (MSDA) to progressively reduce the feature distribution gap through hierarchical subspace modeling. We also introduce a Dual-Domain Synchronous Optimization (DDSO) strategy that jointly leverages synthetic supervision and adaptation to the real domain in a unified training scheme. Extensive experiments underscore the superiority of our approach and its excellence on no-reference image quality metrics.

In real-world scenarios, the haze presents diversity and complexity. However, current dehazing researches usually focus solely on specific categories or the removal of common white haze, frequently lacking the ability to adapt across various unknown haze types. In this study, our emphasis is on constructing a model that shows excellent adaptability across diverse haze conditions. Unlike approaches that solely rely on network structure design to enhance model adaptability, we comprehensively improve dehazing model adaptability from three key aspects: constructing the multitype haze dataset from designed haze degradation models, designing the network architecture, and formulating training strategies suitable for cross-scene generalization. Firstly, to meet the diverse haze training data requirements, we design a multitype haze degradation model to generate more realistic pairs of hazy images. Secondly, to ensure thorough haze removal and natural restoration of texture details in the recovered images, we construct a dual-branch ensemble network framework by leveraging pre-trained clear image prior features and the characteristics of 2D discrete wavelet priors. Finally, to further enhance the adaptability for removing various types of haze, we employ a sample reweighting decorrelation strategy during the network training phase to eliminate dependencies between haze and haze-free background features. Through extensive experiments, our approach shows remarkable performance across diverse haze scenarios. Our method not only outperforms state-of-the-art scene-specific dehazing methods in typical scenarios like daytime and nighttime, but it also excels in handling challenging scenarios such as dusty conditions, and color haze. See more resultshttps://github.com/fyxnl/Image-dehazing-CGID.