Shih-Li Lu, S. Miaou, Shyang-En Weng, Ying-Cheng Lin
{"title":"利用立体深度估计网络和激光雷达辅助相机进行去雾","authors":"Shih-Li Lu, S. Miaou, Shyang-En Weng, Ying-Cheng Lin","doi":"10.1109/ICASI57738.2023.10179550","DOIUrl":null,"url":null,"abstract":"Dehazing research is crucial to ensuring the safety of autonomous driving. To estimate the scattering coefficient of the scene, we use the point cloud produced by LiDAR. To acquire a more precise scene depth, we employ a stereo depth network. Finally, we dehaze the image using the transmission map of the atmospheric scattering model and the atmospheric light value. Experimental results show that the proposed dehazing method works better in object detection than previous dehazing methods.","PeriodicalId":281254,"journal":{"name":"2023 9th International Conference on Applied System Innovation (ICASI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using Stereo Depth Estimation Network and LiDAR-Assisted Camera for Dehazing\",\"authors\":\"Shih-Li Lu, S. Miaou, Shyang-En Weng, Ying-Cheng Lin\",\"doi\":\"10.1109/ICASI57738.2023.10179550\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dehazing research is crucial to ensuring the safety of autonomous driving. To estimate the scattering coefficient of the scene, we use the point cloud produced by LiDAR. To acquire a more precise scene depth, we employ a stereo depth network. Finally, we dehaze the image using the transmission map of the atmospheric scattering model and the atmospheric light value. Experimental results show that the proposed dehazing method works better in object detection than previous dehazing methods.\",\"PeriodicalId\":281254,\"journal\":{\"name\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASI57738.2023.10179550\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Applied System Innovation (ICASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASI57738.2023.10179550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Stereo Depth Estimation Network and LiDAR-Assisted Camera for Dehazing
Dehazing research is crucial to ensuring the safety of autonomous driving. To estimate the scattering coefficient of the scene, we use the point cloud produced by LiDAR. To acquire a more precise scene depth, we employ a stereo depth network. Finally, we dehaze the image using the transmission map of the atmospheric scattering model and the atmospheric light value. Experimental results show that the proposed dehazing method works better in object detection than previous dehazing methods.