Generative adversarial network-based atmospheric scattering model for image dehazing - Jinxiu Zhu; Leilei Meng; Wenxia Wu; Dongmin Choi; Jianjun Ni (Digital Communications and Networks, 2021)
This paper presents a trainable Generative Adversarial Network (GAN)-based end-to-end system for image dehazing, which is named the DehazeGAN. DehazeGAN can be used for edge computing-based applications, such as roadside monitoring. It adopts two networks: one is generator (G), and the other is discriminator (D). The G adopts the U-Net architecture, whose layers are particularly designed to incorporate the atmospheric scattering model of image dehazing. By using a reformulated atmospheric scattering model, the weights of the generator network are initialized by the coarse transmission map, and the biases are adaptively adjusted by using the previous round’s trained weights. Since the details may be blurry after the fog is removed, the contrast loss is added to enhance the visibility actively. Aside from the typical GAN adversarial loss, the pixel-wise Mean Square Error (MSE) loss, the contrast loss and the dark channel loss are introduced into the generator loss function. Extensive experiments on benchmark images, the results of which are compared with those of several state-of-the-art methods, demonstrate that the proposed DehazeGAN performs better and is more effective.