{"title":"亚衍射成像的对抗传感","authors":"Brandon Yushan Feng, Christopher A. Metzler","doi":"10.1364/cosi.2022.cf2c.3","DOIUrl":null,"url":null,"abstract":"We propose a self-supervised learning-based framework for reconstructing images from partially unknown and non-linear measurements. We apply our technique, which is based on matching the distributions of real and simulated observations, to long-range Fourier Ptychography.","PeriodicalId":286361,"journal":{"name":"Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adversarial Sensing for Sub-Diffraction Imaging\",\"authors\":\"Brandon Yushan Feng, Christopher A. Metzler\",\"doi\":\"10.1364/cosi.2022.cf2c.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a self-supervised learning-based framework for reconstructing images from partially unknown and non-linear measurements. We apply our technique, which is based on matching the distributions of real and simulated observations, to long-range Fourier Ptychography.\",\"PeriodicalId\":286361,\"journal\":{\"name\":\"Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/cosi.2022.cf2c.3\",\"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 Congress 2022 (3D, AOA, COSI, ISA, pcAOP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/cosi.2022.cf2c.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose a self-supervised learning-based framework for reconstructing images from partially unknown and non-linear measurements. We apply our technique, which is based on matching the distributions of real and simulated observations, to long-range Fourier Ptychography.