T. Shimobaba, David Blinder, Tobias Birnbaum, I. Hoshi, Harutaka Shiomi, P. Schelkens, T. Ito
{"title":"深度学习计算全息:综述(特邀)","authors":"T. Shimobaba, David Blinder, Tobias Birnbaum, I. Hoshi, Harutaka Shiomi, P. Schelkens, T. Ito","doi":"10.3389/fphot.2022.854391","DOIUrl":null,"url":null,"abstract":"Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.","PeriodicalId":73099,"journal":{"name":"Frontiers in photonics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Deep-Learning Computational Holography: A Review (Invited)\",\"authors\":\"T. Shimobaba, David Blinder, Tobias Birnbaum, I. Hoshi, Harutaka Shiomi, P. Schelkens, T. Ito\",\"doi\":\"10.3389/fphot.2022.854391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.\",\"PeriodicalId\":73099,\"journal\":{\"name\":\"Frontiers in photonics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in photonics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fphot.2022.854391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in photonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fphot.2022.854391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep-Learning Computational Holography: A Review (Invited)
Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.