DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

Abstract

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 nonhomogeneous 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. The source code is available at https://github.com/ liuh127/DW-GAN-Dehazing.

IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing

“…GCA-Net [52] applies gated subnetworks and smooth extended convolutions, which is beneficial for fusing features of different scales and removing possible grid artifacts. DWGAN [53] introduces 2D discrete wavelet transform, aiming at restoring clear texture details and retaining sufficient high-frequency information. GUNet [54] significantly reduces overhead while effectively removing haze.…”

“…Figure 5 shows the dehazing results of some randomly selected synthetic images from the SOTS datasets. DCP [11], Dehaze-Net [14], and DWGAN [53] successfully remove heavy haze, but they exhibit color distortion and increased brightness. There are also issues with brightness enhancement and contrast in the results generated via FFA-Net [36], GCA-Net [52], GUNet [54], and AOD-Net [25].…”