A high-resolution image dehazing GAN model in icing meteorological environment

Abstract

In this paper, we propose a high-resolution GAN model for image dehazing in icing meteorological environment, which strictly follows a physics-driven scattering strategy. First of all, the utilization of sub-pixel convolution realizes the model to remove image artifacts and generate high-resolution images. Secondly, we use Patch-GAN in the discriminator to drive the generator to generate a haze-free image by capturing the details and local information of the image. Furthermore, to restore the texture information of the hazy image and reduce color distortion, the model is jointly trained by multiple loss functions. Experiments show the proposed method achieves advanced performance for image dehazing in icing weather environment.