FISS GAN: A Generative Adversarial Network for Foggy Image Semantic Segmentation

• 作者: Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang
• 来源期刊: 自动化学报（英文版）
• 年，卷（期）: 2021,（8）
• 所属期刊栏目: PAPERS
• 页码范围: 1428-1439
• 页数: 12页
• 语种: 英文
• DOI: 10.1109/JAS.2021.1004057

摘要

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.