We propose a highly generative dehazing method based on pixel-wise Wasserstein autoencoders. In contrast to existing dehazing methods based on generative adversarial networks, our method can produce a variety of dehazed images with different styles. It significantly improves the dehazing accuracy via pixel-wise matching from hazy to dehazed images through 2-dimensional latent tensors of the Wasserstein autoencoder. In addition, we present an advanced feature fusion technique to deliver rich information to the latent space. For style transfer, we introduce a mapping function that transforms existing latent spaces to new ones. Thus, our method can produce highly generative haze-free images with various tones, illuminations, and moods, which induces several interesting applications, including low-light enhancement, daytime dehazing, nighttime dehazing, and underwater image enhancement. Experimental results demonstrate that our method quantitatively outperforms existing state-of-the-art methods for synthetic and real-world datasets, and simultaneously generates highly generative haze-free images, which are qualitatively diverse.

中文翻译：

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用于高生成去雾的逐像素 Wasserstein 自动编码器


我们提出了一种基于像素级 Wasserstein 自动编码器的高度生成去雾方法。与现有的基于生成对抗网络的去雾方法相比，我们的方法可以产生各种不同风格的去雾图像。它通过 Wasserstein 自动编码器的二维潜在张量从有雾图像到去雾图像进行像素级匹配，从而显着提高了去雾精度。此外，我们提出了一种先进的特征融合技术，可以向潜在空间传递丰富的信息。对于风格迁移，我们引入了一个映射函数，可以将现有的潜在空间转换为新的潜在空间。因此，我们的方法可以生成具有各种色调、照明和情绪的高度生成的无雾图像，这引发了一些有趣的应用，包括低光增强、白天除雾、夜间除雾和水下图像增强。实验结果表明，我们的方法在数量上优于合成和现实世界数据集的现有最先进方法，并同时生成高度生成的无雾图像，这些图像在质量上是多样化的。