在實際有霧場景中的影像去霧任務一直面臨著挑戰。現有的影像去霧方法因合成和實際場景中的有霧影像之間存在著巨大的領域差距，降低了實際環境中去霧的性能。然而，也因為必須在相同條件下取得有霧影像和其成對的乾淨影像，因此收集用於訓練去霧模型的實際影像數據集具有挑戰性。在本文中，我們提出了一種基於物理引導的霧增強網路（PANet），它可以生成逼真的有霧影像和其成對的乾淨影像，以有效提升實際場景中的去霧性能。PANet包括一個Haze-to-Parmeter Mapper（HPM），將有霧影像映射到參數空間，以及一個Parameter-to-Haze Mapper（PHM），將重新採樣的霧參數映射回到有霧影像。在參數空間中，我們可以對個別的霧參數圖進行像素級的重新採樣，生成原始訓練集中未出現過且具有物理可解釋性的不同霧條件的多樣化有霧影像。我們的實驗結果表明，PANet可以升成多樣化且符合實際物理特性的有霧影像，豐富現有的霧影像基準，從而有效提升最先進的影像去霧模型的性能。

Image dehazing faces challenges when dealing with hazy images in real-world scenarios. A huge domain gap between synthetic and real-world haze images degrades dehazing performance in practical settings. However, collecting real-world image datasets for training dehazing models is challenging since both hazy and clean pairs must be captured under the same conditions. In this paper, we propose a Physics-guided Parametric Augmentation Network (PANet) that generates photo-realistic hazy and clean training pairs to effectively enhance real-world dehazing performance. PANet comprises a Haze-to-Parameter Mapper (HPM) to project hazy images into a parameter space and a Parameter-to-Haze Mapper (PHM) to map the resampled haze parameters back to hazy images. In the parameter space, we can pixel-wisely resample individual haze parameter maps to generate diverse hazy images with physically-explainable haze conditions unseen in the training set. Our experimental results demonstrate that PANet can augment diverse realistic hazy images to enrich existing hazy image benchmarks so as to effectively boost the performances of state-of-the-art image dehazing models.

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