Chenghuan Wang, Z. Meng, Ronglei Xie, Xiaoai Jiang
{"title":"基于循环gan的单幅图像去雾算法","authors":"Chenghuan Wang, Z. Meng, Ronglei Xie, Xiaoai Jiang","doi":"10.1145/3366194.3366237","DOIUrl":null,"url":null,"abstract":"Due to the effect of atmospheric light scattering, the quality of images taken under haze weather conditions will be seriously degraded. These characteristics affect the judgment and extraction of image features and reduce the application value of images. Image dehazing is therefore a necessary step in many computer vision tasks. In this paper, an end-to-end image dehazing algorithm Dehaze-GAN based on Cycle-GAN is proposed. In order to ensure that the structure of the images before and after dehazing is basically the same, the algorithm adds structure consistency loss on the basis of Cycle-GAN. The input of Dehaze-GAN is a hazy image and the output is a clean image. Dehaze-GAN is trained by a large number of hazy images and their corresponding clean images. The experimental results show that Dehaze-GAN is superior to other dehazing algorithms in both PSNR and SSIM dehazing performance indicators.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Single Image Dehazing Algorithm Based on Cycle-GAN\",\"authors\":\"Chenghuan Wang, Z. Meng, Ronglei Xie, Xiaoai Jiang\",\"doi\":\"10.1145/3366194.3366237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the effect of atmospheric light scattering, the quality of images taken under haze weather conditions will be seriously degraded. These characteristics affect the judgment and extraction of image features and reduce the application value of images. Image dehazing is therefore a necessary step in many computer vision tasks. In this paper, an end-to-end image dehazing algorithm Dehaze-GAN based on Cycle-GAN is proposed. In order to ensure that the structure of the images before and after dehazing is basically the same, the algorithm adds structure consistency loss on the basis of Cycle-GAN. The input of Dehaze-GAN is a hazy image and the output is a clean image. Dehaze-GAN is trained by a large number of hazy images and their corresponding clean images. The experimental results show that Dehaze-GAN is superior to other dehazing algorithms in both PSNR and SSIM dehazing performance indicators.\",\"PeriodicalId\":105852,\"journal\":{\"name\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3366194.3366237\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366194.3366237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Single Image Dehazing Algorithm Based on Cycle-GAN
Due to the effect of atmospheric light scattering, the quality of images taken under haze weather conditions will be seriously degraded. These characteristics affect the judgment and extraction of image features and reduce the application value of images. Image dehazing is therefore a necessary step in many computer vision tasks. In this paper, an end-to-end image dehazing algorithm Dehaze-GAN based on Cycle-GAN is proposed. In order to ensure that the structure of the images before and after dehazing is basically the same, the algorithm adds structure consistency loss on the basis of Cycle-GAN. The input of Dehaze-GAN is a hazy image and the output is a clean image. Dehaze-GAN is trained by a large number of hazy images and their corresponding clean images. The experimental results show that Dehaze-GAN is superior to other dehazing algorithms in both PSNR and SSIM dehazing performance indicators.