用物理模型迁移雾霾特征，让去雾模型适应新场景。
PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
- 通过物理引导迁移雾霾模式，生成适配目标域的微调数据。
- 在多个真实图像去雾数据集上显著提升现有模型性能。
- 适合需要快速适配新环境的实时去雾应用。
图像去雾旨在消除图像中的雾霾伪影。尽管先前研究收集了成对的真实世界有雾与无雾图像以提升模型在真实场景下的表现，但这些模型在处理未见过的真实世界有雾图像时仍会显著性能下降，原因在于训练数据有限。为此，我们提出一种灵活的域适应方法，以增强测试时的去雾性能。观察到预测雾霾分布比恢复清晰内容更容易，我们提出物理引导的雾霾迁移网络（PHATNet），将未见目标域的雾霾特征迁移到源域无雾图像上，生成特定域的微调数据集，用于更新去雾模型实现有效域适应。此外，引入雾霾迁移一致性损失和内容泄漏损失，提升PHATNet的特征解耦能力。实验结果表明，PHATNet显著提升了基准真实世界图像去雾数据集上的先进去雾模型性能。
原文摘要 · Abstract (English)
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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