  Deep learning-based methods have achieved considerable success on single
image dehazing in recent years. However, these methods are often subject to
performance degradation when domain shifts are confronted. Specifically, haze
density gaps exist among the existing datasets, often resulting in poor
performance when these methods are tested across datasets. To address this
issue, we propose a density-aware mixup augmentation (DAMix). DAMix generates
samples in an attempt to minimize the Wasserstein distance with the hazy images
in the target domain. These DAMix-ed samples not only mitigate domain gaps but
are also proven to comply with the atmospheric scattering model. Thus, DAMix
achieves comprehensive improvements on domain adaptation. Furthermore, we show
that DAMix is helpful with respect to data efficiency. Specifically, a network
trained with half of the source dataset using DAMix can achieve even better
adaptivity than that trained with the whole source dataset but without DAMix.
