  Image dehazing using learning-based methods has achieved state-of-the-art
performance in recent years. However, most existing methods train a dehazing
model on synthetic hazy images, which are less able to generalize well to real
hazy images due to domain shift. To address this issue, we propose a domain
adaptation paradigm, which consists of an image translation module and two
image dehazing modules. Specifically, we first apply a bidirectional
translation network to bridge the gap between the synthetic and real domains by
translating images from one domain to another. And then, we use images before
and after translation to train the proposed two image dehazing networks with a
consistency constraint. In this phase, we incorporate the real hazy image into
the dehazing training via exploiting the properties of the clear image (e.g.,
dark channel prior and image gradient smoothing) to further improve the domain
adaptivity. By training image translation and dehazing network in an end-to-end
manner, we can obtain better effects of both image translation and dehazing.
Experimental results on both synthetic and real-world images demonstrate that
our model performs favorably against the state-of-the-art dehazing algorithms.
