Image dehazing based on Uformer modified WGAN

In hazy weather, the obtained images of optical instruments are severely degraded due to the multiple atmospheric light scattering, which will significantly influence subsequent image processing such as target recognition and location. In this paper, we propose an efficient image dehazing network based on the framework of the Wasserstein generative adversarial network (WGAN). Inspired by the classic U-net network architecture, we first use a transformer-based image restoration architecture Uformer to modify the generator of WGAN. Then for loss function design, according to the requirement of the image dehazing task, the overall network training is constrained from two aspects, i.e., the pixel loss and the adversarial loss. Finally, the synthetic haze dataset was used to train and evaluate the effectiveness of the network. The results show that the proposed method can obtain high quality restored images, which is comparable to some current methods.