Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration. In addition, we design a densely connected block to capture global and local information for dehazing and semantic prior estimation. To eliminate the unnatural appearance of some objects, we propose to fuse the features from shallow and deep layers adaptively. Experimental results demonstrate that our proposed model performs favorably against the state-of-the-art single image dehazing approaches.

中文翻译：

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具有自适应特征融合的语义感知去雾网络

尽管卷积神经网络 (CNN) 已经显示出对单幅图像去雾的高质量重建，但由于模糊场景中的语义混淆，恢复自然和逼真的去雾结果仍然是一个具有挑战性的问题。在本文中，我们展示了通过将语义先验结合到去雾网络中可以忠实地恢复纹理，因为无雾图像中的对象往往会显示特定的形状、纹理和颜色。我们提出了一种语义感知去雾网络（SDNet），其中将语义先验作为去雾的颜色约束，有利于获得合理的场景配置。此外，我们设计了一个密集连接的块来捕获全局和局部信息，用于去雾和语义先验估计。为了消除某些物体的不自然外观，我们建议自适应地融合浅层和深层的特征。实验结果表明，我们提出的模型优于最先进的单图像去雾方法。