(Peer-Reviewed) Lensless complex amplitude demodulation based on deep learning in holographic data storage
Jianying Hao 郝建颖 ¹ ³, Xiao Lin 林枭 ¹, Yongkun Lin 林雍坤 ¹, Mingyong Chen 陈明勇 ¹, Ruixian Chen 陈瑞娴 ¹, Guohai Situ 司徒国海 ², Hideyoshi Horimai ³, Xiaodi Tan 谭小地 ¹
¹ College of Photonic and Electronic Engineering, Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350117, China
中国 福州 福建师范大学光电与信息工程学院 医学光电科学与技术教育部重点实验室 福建省光子技术重点实验室 福建省光电传感应用工程技术研究中心
² Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
³ HolyMine Corporation, 2032-2-301 Ooka, Numazu, Shizuoka 410-0022, Japan
Opto-Electronic Advances, 2023-03-25
To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase.
By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively.
The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.
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