PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
This addresses the domain shift issue in real-world image dehazing for computer vision applications, representing an incremental improvement by enhancing existing models through domain adaptation.
The paper tackles the problem of domain adaptation for real-world image dehazing, where models trained on limited paired data perform poorly on unseen hazy images, and proposes PHATNet to transfer haze patterns from target to source domains, creating fine-tuning sets that significantly boost state-of-the-art models on benchmark datasets.
Image dehazing aims to remove unwanted hazy artifacts in images. Although previous research has collected paired real-world hazy and haze-free images to improve dehazing models' performance in real-world scenarios, these models often experience significant performance drops when handling unseen real-world hazy images due to limited training data. This issue motivates us to develop a flexible domain adaptation method to enhance dehazing performance during testing. Observing that predicting haze patterns is generally easier than recovering clean content, we propose the Physics-guided Haze Transfer Network (PHATNet) which transfers haze patterns from unseen target domains to source-domain haze-free images, creating domain-specific fine-tuning sets to update dehazing models for effective domain adaptation. Additionally, we introduce a Haze-Transfer-Consistency loss and a Content-Leakage Loss to enhance PHATNet's disentanglement ability. Experimental results demonstrate that PHATNet significantly boosts state-of-the-art dehazing models on benchmark real-world image dehazing datasets.