In this paper, we propose a novel image dehazing method. Typical deep learning models for dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be effective for indoor image dehazing but less so for outdoor images. We propose a heterogeneous Generative Adversarial Networks (GAN) based method composed of a cycle-consistent Generative Adversarial Networks (CycleGAN) for producing haze-clear images and a conditional Generative Adversarial Networks (cGAN) for preserving textural details. We introduce a novel loss function in the training of the fused network to minimize GAN generated artifacts, to recover fine details, and to preserve color components. These networks are fused via a convolutional neural network (CNN) to generate dehazed image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on both synthetic and real-world hazy images.

中文翻译：

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用于单图像去雾的异构对抗网络融合


在本文中，我们提出了一种新颖的图像去雾方法。典型的去雾深度学习模型是在配对的合成室内数据集上进行训练的。因此，这些模型对于室内图像去雾可能有效，但对于室外图像则效果较差。我们提出了一种基于异构生成对抗网络（GAN）的方法，由用于生成雾霾清晰图像的循环一致生成对抗网络（CycleGAN）和用于保留纹理细节的条件生成对抗网络（cGAN）组成。我们在融合网络的训练中引入了一种新颖的损失函数，以最大限度地减少 GAN 生成的伪影、恢复精细细节并保留颜色分量。这些网络通过卷积神经网络 (CNN) 融合以生成去雾图像。大量的实验表明，所提出的方法在合成和真实的模糊图像上都显着优于最先进的方法。