Physical-prior-guided single image dehazing network via unpaired contrastive learning

Image dehazing aims to restore high fidelity clear images from hazy ones. It has wide applications on many intelligent image analysis systems in computer vision area. Many prior-based and learning-based methods have already made significant progress in this field. However, the domain gap between synthetic and real hazy images still negatively impacts model’s generalization performance in real-world scenarios. In this paper, we have proposed an effective physical-prior-guided single image dehazing network via unpaired contrastive learning (PDUNet). The learning process of PDUNet consists of pre-training stage on synthetic data and fine-tuning stage on real data. Mixed-prior modules, controllable zero-convolution modules, and unpaired contrastive regularization with hybrid transmission maps have been proposed to fully utilize complementary advantages of both prior-based and learning-based strategies. Specifically, mixed-prior module provides precise haze distributions. Zero-convolution modules serving as controllable bypass supplement pre-trained model with additional real-world haze information, as well as mitigate the risk of catastrophic forgetting during fine-tuning. Hybrid prior-generated transmission maps are employed for unpaired contrastive regularization. Through leveraging physical prior statistics and vast of unlabel real-data, the proposed PDUNet exhibits excellent generalization and adaptability on handling real-world hazy scenarios. Extensive experiments on public dataset have demonstrated that the proposed method improves PSNR,NIQE and BRISQUE values by an average of 0.33, 0.69 and 2.3, respectively, with comparable model efficiency compared to SOTA. Related codes and model parameters will be publicly available on Github https://github.com/Jotra9872/PDU-Net.