Because pixel values of foggy images are irregularly higher than those of images captured in normal weather (clear images), it is difficult to extract and express their texture. No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images. We investigated this relationship and propose a generative adversarial network (GAN) for foggy image semantic segmentation (FISS GAN), which contains two parts: an edge GAN and a semantic segmentation GAN. The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN. The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images. Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

中文翻译：

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FISS GAN：用于雾图像语义分割的生成对抗网络


由于雾天图像的像素值不规则地高于正常天气下拍摄的图像（清晰图像），因此难以提取和表达其纹理。之前还没有开发出直接探索雾图像和语义分割图像之间关系的方法。我们研究了这种关系，并提出了一种用于雾图像语义分割的生成对抗网络（GAN）（FISS GAN），它包含两部分：边缘 GAN 和语义分割 GAN。边缘GAN旨在从有雾图像中生成边缘信息，为语义分割GAN提供辅助信息。语义分割GAN旨在提取和表达有雾图像的纹理并生成语义分割图像。在雾天城市景观数据集和雾天驾驶数据集上的实验表明，FISS GAN 取得了最先进的性能。