{"title":"低光子条件下物理嵌入深度神经网络的相位检索","authors":"Mo Deng, A. Goy, K. Arthur, G. Barbastathis","doi":"10.1364/COSI.2019.CM1A.2","DOIUrl":null,"url":null,"abstract":"We design a deep neural network where the known physical forward operator is explicitly embedded and apply it to phase retrieval under low photon conditions to achieve better performance over the end-to-end approach.","PeriodicalId":123636,"journal":{"name":"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Physics Embedded Deep Neural Network for Phase Retrieval under Low Photon Conditions\",\"authors\":\"Mo Deng, A. Goy, K. Arthur, G. Barbastathis\",\"doi\":\"10.1364/COSI.2019.CM1A.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We design a deep neural network where the known physical forward operator is explicitly embedded and apply it to phase retrieval under low photon conditions to achieve better performance over the end-to-end approach.\",\"PeriodicalId\":123636,\"journal\":{\"name\":\"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/COSI.2019.CM1A.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/COSI.2019.CM1A.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Physics Embedded Deep Neural Network for Phase Retrieval under Low Photon Conditions
We design a deep neural network where the known physical forward operator is explicitly embedded and apply it to phase retrieval under low photon conditions to achieve better performance over the end-to-end approach.
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