{"title":"时间分辨发射的机器学习:图像分辨率增强","authors":"S. Chef, C. T. Chua, Chee Lip Gan","doi":"10.31399/asm.edfa.2021-3.p024","DOIUrl":null,"url":null,"abstract":"\n This article describes a novel method for improving image resolution achieved using time-resolved photon emission techniques. Instead of directly generating images from photon counting, all detected photons are displayed as a point cloud in 3D space and a new higher-resolution image is generated based on probability density functions associated with photon distributions. Unsupervised learning algorithms identify photon distribution patterns as well as fainter emission sources.","PeriodicalId":431761,"journal":{"name":"EDFA Technical Articles","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for Time-Resolved Emission: Image Resolution Enhancement\",\"authors\":\"S. Chef, C. T. Chua, Chee Lip Gan\",\"doi\":\"10.31399/asm.edfa.2021-3.p024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This article describes a novel method for improving image resolution achieved using time-resolved photon emission techniques. Instead of directly generating images from photon counting, all detected photons are displayed as a point cloud in 3D space and a new higher-resolution image is generated based on probability density functions associated with photon distributions. Unsupervised learning algorithms identify photon distribution patterns as well as fainter emission sources.\",\"PeriodicalId\":431761,\"journal\":{\"name\":\"EDFA Technical Articles\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EDFA Technical Articles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31399/asm.edfa.2021-3.p024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EDFA Technical Articles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31399/asm.edfa.2021-3.p024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning for Time-Resolved Emission: Image Resolution Enhancement
This article describes a novel method for improving image resolution achieved using time-resolved photon emission techniques. Instead of directly generating images from photon counting, all detected photons are displayed as a point cloud in 3D space and a new higher-resolution image is generated based on probability density functions associated with photon distributions. Unsupervised learning algorithms identify photon distribution patterns as well as fainter emission sources.
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