{"title":"基于深度学习的相位检索混合方法","authors":"Çaǧatay Işıl, F. Oktem, Aykut Koç","doi":"10.1364/COSI.2019.CTH2C.5","DOIUrl":null,"url":null,"abstract":"We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.","PeriodicalId":123636,"journal":{"name":"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Deep Learning-Based Hybrid Approach for Phase Retrieval\",\"authors\":\"Çaǧatay Işıl, F. Oktem, Aykut Koç\",\"doi\":\"10.1364/COSI.2019.CTH2C.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.\",\"PeriodicalId\":123636,\"journal\":{\"name\":\"Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"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.CTH2C.5\",\"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.CTH2C.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning-Based Hybrid Approach for Phase Retrieval
We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
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