{"title":"利用电子编码和衍射解码通过随机未知漫射体的光学信息传输","authors":"Yuhang Li, Tianyi Gan, Bijie Bai, Çaǧatay Işıl, M. Jarrahi, Aydogan Ozcan","doi":"10.1117/1.AP.5.4.046009","DOIUrl":null,"url":null,"abstract":"Abstract. Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.","PeriodicalId":33241,"journal":{"name":"Advanced Photonics","volume":null,"pages":null},"PeriodicalIF":20.6000,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding\",\"authors\":\"Yuhang Li, Tianyi Gan, Bijie Bai, Çaǧatay Işıl, M. Jarrahi, Aydogan Ozcan\",\"doi\":\"10.1117/1.AP.5.4.046009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.\",\"PeriodicalId\":33241,\"journal\":{\"name\":\"Advanced Photonics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":20.6000,\"publicationDate\":\"2023-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Photonics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1117/1.AP.5.4.046009\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Photonics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1117/1.AP.5.4.046009","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
Abstract. Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.
期刊介绍:
Advanced Photonics is a highly selective, open-access, international journal that publishes innovative research in all areas of optics and photonics, including fundamental and applied research. The journal publishes top-quality original papers, letters, and review articles, reflecting significant advances and breakthroughs in theoretical and experimental research and novel applications with considerable potential.
The journal seeks high-quality, high-impact articles across the entire spectrum of optics, photonics, and related fields with specific emphasis on the following acceptance criteria:
-New concepts in terms of fundamental research with great impact and significance
-State-of-the-art technologies in terms of novel methods for important applications
-Reviews of recent major advances and discoveries and state-of-the-art benchmarking.
The journal also publishes news and commentaries highlighting scientific and technological discoveries, breakthroughs, and achievements in optics, photonics, and related fields.