{"title":"用智能光学技术看到看不见的物体","authors":"Isaac Nape, Andrew Forbes","doi":"10.1038/s41377-024-01575-2","DOIUrl":null,"url":null,"abstract":"<p><p>Transparent objects are invisible to traditional cameras because they can only detect intensity fluctuations, necessitating the need for interferometry followed by computationally intensive digital image processing. Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics, for a direct in-situ measurement of transparent objects with conventional cameras.</p>","PeriodicalId":18093,"journal":{"name":"Light, science & applications","volume":"13 1","pages":"232"},"PeriodicalIF":19.4000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11374888/pdf/","citationCount":"0","resultStr":"{\"title\":\"Seeing invisible objects with intelligent optics.\",\"authors\":\"Isaac Nape, Andrew Forbes\",\"doi\":\"10.1038/s41377-024-01575-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Transparent objects are invisible to traditional cameras because they can only detect intensity fluctuations, necessitating the need for interferometry followed by computationally intensive digital image processing. Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics, for a direct in-situ measurement of transparent objects with conventional cameras.</p>\",\"PeriodicalId\":18093,\"journal\":{\"name\":\"Light, science & applications\",\"volume\":\"13 1\",\"pages\":\"232\"},\"PeriodicalIF\":19.4000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11374888/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Light, science & applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1038/s41377-024-01575-2\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Light, science & applications","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1038/s41377-024-01575-2","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Physics and Astronomy","Score":null,"Total":0}
Transparent objects are invisible to traditional cameras because they can only detect intensity fluctuations, necessitating the need for interferometry followed by computationally intensive digital image processing. Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics, for a direct in-situ measurement of transparent objects with conventional cameras.
期刊介绍：
Light: Science & Applications is an open-access, fully peer-reviewed publication.It publishes high-quality optics and photonics research globally, covering fundamental research and important issues in engineering and applied sciences related to optics and photonics.
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