We present an effective semi-supervised learning algorithm for single image dehazing. The proposed algorithm applies a deep Convolutional Neural Network (CNN) containing a supervised learning branch and an unsupervised learning branch. In the supervised branch, the deep neural network is constrained by the supervised loss functions, which are mean squared, perceptual, and adversarial losses. In the unsupervised branch, we exploit the properties of clean images via sparsity of dark channel and gradient priors to constrain the network. We train the proposed network on both the synthetic data and real-world images in an end-to-end manner. Our analysis shows that the proposed semi-supervised learning algorithm is not limited to synthetic training datasets and can be generalized well to real-world images. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art single image dehazing algorithms on both benchmark datasets and real-world images.

中文翻译：

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半监督图像去雾。


我们提出了一种有效的单图像去雾半监督学习算法。该算法应用包含监督学习分支和无监督学习分支的深度卷积神经网络（CNN）。在监督分支中，深度神经网络受到监督损失函数的约束，这些损失函数是均方损失、感知损失和对抗损失。在无监督分支中，我们通过暗通道的稀疏性和梯度先验来利用干净图像的特性来约束网络。我们以端到端的方式在合成数据和真实图像上训练所提出的网络。我们的分析表明，所提出的半监督学习算法不仅限于合成训练数据集，而且可以很好地推广到现实世界的图像。大量的实验结果表明，所提出的算法在基准数据集和真实世界图像上的性能优于最先进的单图像去雾算法。