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.

本文提出了一种物理引导的参数增强网络 (PANet)，用于生成逼真的有雾和无雾训练对，以有效提升实际环境下的图像去雾性能。实验结果表明，PANet 可以增加多样逼真的有雾图像，丰富现有的有雾图像基准，从而有效提升最先进的图像去雾模型的性能。