{"title":"基于深度学习的高阶像差全息传感器。","authors":"Ming Liu, Bing Dong","doi":"10.1364/AO.574070","DOIUrl":null,"url":null,"abstract":"<p><p>A deep learning-enhanced holographic wavefront sensor (DLHWS) is proposed to overcome the limitations of conventional holographic modal wavefront sensors (HMWS). Traditional HMWS, based on the second-moment-intensity (SMI-HMWS), suffers from measurement inaccuracies due to speckle noise from kinoform computer-generated holograms (CGHs) and restricted measurable modes. The DLHWS utilizes deep neural networks to process multiple biased images generated by a CGH, either a lightweight convolutional neural network (CNN) for modal coefficient estimation (DLHWS-c) or a UNet for direct phase map reconstruction (DLHWS-p). Simulations and experiments demonstrate that DLHWS significantly improves wavefront sensing accuracy and capability to detect high-order aberrations. DLHWS-c offers superior inference speed and high accuracy for low-order modes. In contrast, DLHWS-p delivers higher precision in capturing high-order aberrations comprising hundreds of modes induced by atmospheric turbulence but requires greater computational resources.</p>","PeriodicalId":101299,"journal":{"name":"Applied optics","volume":"64 27","pages":"8130-8138"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning-enhanced holographic wavefront sensor for high-order aberration sensing.\",\"authors\":\"Ming Liu, Bing Dong\",\"doi\":\"10.1364/AO.574070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A deep learning-enhanced holographic wavefront sensor (DLHWS) is proposed to overcome the limitations of conventional holographic modal wavefront sensors (HMWS). Traditional HMWS, based on the second-moment-intensity (SMI-HMWS), suffers from measurement inaccuracies due to speckle noise from kinoform computer-generated holograms (CGHs) and restricted measurable modes. The DLHWS utilizes deep neural networks to process multiple biased images generated by a CGH, either a lightweight convolutional neural network (CNN) for modal coefficient estimation (DLHWS-c) or a UNet for direct phase map reconstruction (DLHWS-p). Simulations and experiments demonstrate that DLHWS significantly improves wavefront sensing accuracy and capability to detect high-order aberrations. DLHWS-c offers superior inference speed and high accuracy for low-order modes. In contrast, DLHWS-p delivers higher precision in capturing high-order aberrations comprising hundreds of modes induced by atmospheric turbulence but requires greater computational resources.</p>\",\"PeriodicalId\":101299,\"journal\":{\"name\":\"Applied optics\",\"volume\":\"64 27\",\"pages\":\"8130-8138\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/AO.574070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/AO.574070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep learning-enhanced holographic wavefront sensor for high-order aberration sensing.
A deep learning-enhanced holographic wavefront sensor (DLHWS) is proposed to overcome the limitations of conventional holographic modal wavefront sensors (HMWS). Traditional HMWS, based on the second-moment-intensity (SMI-HMWS), suffers from measurement inaccuracies due to speckle noise from kinoform computer-generated holograms (CGHs) and restricted measurable modes. The DLHWS utilizes deep neural networks to process multiple biased images generated by a CGH, either a lightweight convolutional neural network (CNN) for modal coefficient estimation (DLHWS-c) or a UNet for direct phase map reconstruction (DLHWS-p). Simulations and experiments demonstrate that DLHWS significantly improves wavefront sensing accuracy and capability to detect high-order aberrations. DLHWS-c offers superior inference speed and high accuracy for low-order modes. In contrast, DLHWS-p delivers higher precision in capturing high-order aberrations comprising hundreds of modes induced by atmospheric turbulence but requires greater computational resources.