Outside the house, images taken using a phone in foggy weather are not suitable for automation due to low contrast. Usually, it is revised in the dark channel prior (DCP) method (K. He et al. 2009), but the non-sky bright area exists due to mistakes in the removal. In this paper, we propose an algorithm, defog-based generative adversarial network (DbGAN). We use generative adversarial network (GAN) for training and embed target map (TM) in the anti-network generator, only the part of bright area layer of image, in local attention model image training and testing in deep learning, and the effective processing of the wrong removal part is achieved, thus better restoring the defog image. Then, the DCP method obtains a good defog visual effect, and the evaluation index peak signal-to-noise ratio (PSNR) is used to make a judgment; the simulation result is consistent with the visual effect. We proved the DbGAN is a practical import of target map in the GAN. The algorithm is used defogging in the highlighted area is well realized, which makes up for the shortcomings of the DCP algorithm.

中文翻译：

---

基于条件生成对抗网络的单图像除雾算法

在房子外面，由于雾度低，在大雾天气中使用电话拍摄的图像不适合自动化。通常，使用暗通道先验（DCP）方法对其进行修改（K. He等，2009），但是由于去除错误而存在非天空明亮区域。在本文中，我们提出了一种基于除雾的生成对抗网络（DbGAN）算法。我们使用生成对抗网络（GAN）进行训练，并将目标地图（TM）嵌入反网络生成器中，仅将图像的明亮区域层的一部分嵌入到局部学习模型的图像训练和深度学习测试中，并进行有效的处理去除了错误的去除部分，从而更好地恢复了除雾图像。然后，DCP方法获得良好的除雾视觉效果，并使用评估指标峰值信噪比（PSNR）进行判断；仿真结果与视觉效果一致。我们证明了DbGAN是GAN中目标地图的实用导入。该算法用于高亮区域的除雾效果很好，弥补了DCP算法的不足。