{"title":"利用深度学习去噪技术推进 X 射线检测工作","authors":"Sara Ziliani","doi":"10.58286/29550","DOIUrl":null,"url":null,"abstract":"\nX-ray inspection plays a pivotal role in the food and non-destructive testing industries, ensuring optimal products quality and \n\nsafety. To improve contaminants and defects detection, it is crucial to reduce the amount of noise in the acquired images. \n\nAddressing this, Hamamatsu Photonics has developed a new de-noising technology based on deep learning algorithms and \n\nan innovative X-ray simulation method.\n","PeriodicalId":482749,"journal":{"name":"e-Journal of Nondestructive Testing","volume":"23 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advancing X-ray Inspection with Deep Learning De-noising Technology\",\"authors\":\"Sara Ziliani\",\"doi\":\"10.58286/29550\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nX-ray inspection plays a pivotal role in the food and non-destructive testing industries, ensuring optimal products quality and \\n\\nsafety. To improve contaminants and defects detection, it is crucial to reduce the amount of noise in the acquired images. \\n\\nAddressing this, Hamamatsu Photonics has developed a new de-noising technology based on deep learning algorithms and \\n\\nan innovative X-ray simulation method.\\n\",\"PeriodicalId\":482749,\"journal\":{\"name\":\"e-Journal of Nondestructive Testing\",\"volume\":\"23 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"e-Journal of Nondestructive Testing\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.58286/29550\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"e-Journal of Nondestructive Testing","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.58286/29550","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
X 射线检测在食品和无损检测行业中发挥着举足轻重的作用，可确保最佳的产品质量和安全。为了改进污染物和缺陷检测，减少采集图像中的噪声量至关重要。为此，Hamamatsu Photonics 开发了一种基于深度学习算法和创新 X 射线模拟方法的新型去噪技术。
Advancing X-ray Inspection with Deep Learning De-noising Technology
X-ray inspection plays a pivotal role in the food and non-destructive testing industries, ensuring optimal products quality and
safety. To improve contaminants and defects detection, it is crucial to reduce the amount of noise in the acquired images.
Addressing this, Hamamatsu Photonics has developed a new de-noising technology based on deep learning algorithms and
an innovative X-ray simulation method.
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