{"title":"基于CUDA的1亿像素光学系统快速图像拼接设计","authors":"Zhao-yi Tang, Yuan Tian, Zhennan Wang, Mingfei Xu, Ying-Yu Wang, Weiqun Ma, Xiaolong Wang","doi":"10.1145/3424978.3425105","DOIUrl":null,"url":null,"abstract":"To avoid the difficulty of image processing in the gigapixel optical system, this paper proposes a CUDA-based acceleration strategy for the rapid mosaic of a large-scale image and realizes the rapid mosaic of a large-scale image using the collaborative processing of CPU and GPU. Finally, the author designed an electronic hardware and software system and a fast splicing algorithm suitable for the rapid processing of large-scale image-based on a 100-megapixel monocentric multiscale optical system. And the real-time processing of data from nine 12 million pixel sub-cameras at 7.5 frames per second is completed to realize the real-time splicing of the video in the whole field of view.","PeriodicalId":178822,"journal":{"name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of Rapid Image Mosaic Based on CUDA by 100-Megapixel Optical System\",\"authors\":\"Zhao-yi Tang, Yuan Tian, Zhennan Wang, Mingfei Xu, Ying-Yu Wang, Weiqun Ma, Xiaolong Wang\",\"doi\":\"10.1145/3424978.3425105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To avoid the difficulty of image processing in the gigapixel optical system, this paper proposes a CUDA-based acceleration strategy for the rapid mosaic of a large-scale image and realizes the rapid mosaic of a large-scale image using the collaborative processing of CPU and GPU. Finally, the author designed an electronic hardware and software system and a fast splicing algorithm suitable for the rapid processing of large-scale image-based on a 100-megapixel monocentric multiscale optical system. And the real-time processing of data from nine 12 million pixel sub-cameras at 7.5 frames per second is completed to realize the real-time splicing of the video in the whole field of view.\",\"PeriodicalId\":178822,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Computer Science and Application Engineering\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Computer Science and Application Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3424978.3425105\",\"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 4th International Conference on Computer Science and Application Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3424978.3425105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of Rapid Image Mosaic Based on CUDA by 100-Megapixel Optical System
To avoid the difficulty of image processing in the gigapixel optical system, this paper proposes a CUDA-based acceleration strategy for the rapid mosaic of a large-scale image and realizes the rapid mosaic of a large-scale image using the collaborative processing of CPU and GPU. Finally, the author designed an electronic hardware and software system and a fast splicing algorithm suitable for the rapid processing of large-scale image-based on a 100-megapixel monocentric multiscale optical system. And the real-time processing of data from nine 12 million pixel sub-cameras at 7.5 frames per second is completed to realize the real-time splicing of the video in the whole field of view.