PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing

• URL: http://arxiv.org/abs/2507.14826v1
• Date: Sun, 20 Jul 2025 05:26:30 GMT
• Title: PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
• Authors: Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chia-Wen Lin,
• Abstract summary: Previous research has collected paired real-world hazy and haze-free images to improve dehazing models' performance in real-world scenarios.<n>This issue motivates us to develop a flexible domain adaptation method to enhance dehazing performance during testing.<n>We propose the Physics-guided Haze Transfer Network (PHATNet) which transfers haze patterns from unseen target domains to source-domain haze-free images.
• Score: 45.78830437593351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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