  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.
