Convolutional neural networks (CNNs) have attracted much research attention and achieved great improvements in single-image dehazing. However, previous learning-based dehazing methods are mainly trained on synthetic data, which greatly degrades their generalization capability on natural hazy images. To address this issue, this article proposes a semi-supervised learning approach for single-image dehazing, where both synthetic and realistic images are leveraged during training. Considering the situation that it is hard to obtain the realistic pairs of hazy and haze-free images, how to utilize the realistic data is not a trivial work. In this article, a domain alignment module is introduced to narrow the distribution distance between synthetic data and realistic hazy images in a latent feature space. Meanwhile, a haze-aware attention module is designed to describe haze densities of different regions in the image, thus adaptively responds for different hazy areas. Furthermore, the dark channel prior is introduced to the framework to improve the quality of the unsupervised learning results by considering the statistical characters of haze-free images. Such a semi-supervised design can significantly address the domain shift issue between the synthetic and realistic data, and improve generalization performance in the real world. Experiments indicate that the proposed method obtains state-of-the-art performance on both public synthetic and realistic hazy images with better visual results.

中文翻译：

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用于单图像去雾的半监督域对齐学习


卷积神经网络（CNN）引起了广泛的研究关注，并在单图像去雾方面取得了巨大的进步。然而，以前基于学习的去雾方法主要是在合成数据上进行训练，这极大地降低了它们对自然模糊图像的泛化能力。为了解决这个问题，本文提出了一种用于单图像去雾的半监督学习方法，在训练过程中同时利用合成图像和真实图像。考虑到很难获得真实的有雾和无雾图像对的情况，如何利用真实数据并不是一件容易的事。在本文中，引入了域对齐模块来缩小潜在特征空间中合成数据和真实模糊图像之间的分布距离。同时，设计了雾霾感知注意模块来描述图像中不同区域的雾霾密度，从而对不同的雾霾区域做出自适应响应。此外，框架中引入了暗通道先验，通过考虑无雾图像的统计特征来提高无监督学习结果的质量。这种半监督设计可以显着解决合成数据和现实数据之间的域转移问题，并提高现实世界中的泛化性能。实验表明，该方法在公共合成图像和真实模糊图像上均获得了最先进的性能，并具有更好的视觉效果。