  Hazy images are often subject to color distortion, blurring, and other
visible quality degradation. Some existing CNN-based methods have great
performance on removing homogeneous haze, but they are not robust in
non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the
complicated haze distribution, texture details are easy to be lost during the
dehazing process. Secondly, since the training pairs are hard to be collected,
training on limited data can easily lead to over-fitting problem. To tackle
these two issues, we introduce a novel dehazing network using 2D discrete
wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network
to deal with the aforementioned problems. By utilizing wavelet transform in DWT
branch, our proposed method can retain more high-frequency knowledge in feature
maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted
in the knowledge adaptation branch. Owing to the robust feature representations
of ImageNet pre-training, the generalization ability of our network is improved
dramatically. Finally, a patch-based discriminator is used to reduce artifacts
of the restored images. Extensive experimental results demonstrate that the
proposed method outperforms the state-of-the-arts quantitatively and
qualitatively.
