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Lensless complex amplitude demodulation based on deep learning in holographic data storage
全息数据存储中基于深度学习的无透镜复振幅解调
ホログラフィックデータ記憶における深さ学習に基づくレンズレス複素振幅復調
홀로그램 데이터 저장소의 딥 러닝 기반 렌즈 없는 복진폭 변조
Demodulación de amplitud compleja sin lente basada en aprendizaje profundo en el almacenamiento de datos holográficos
Démodulation d'amplitude complexe sans lentille basée sur le Deep Learning dans le stockage de données holographique
Безлинзовая комплексная амплитудная демодуляция в голографическом хранилище данных на основе глубокого обучения
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
中国科学院上海光学精密机械研究所
³ HolyMine Corporation, 2032-2-301 Ooka, Numazu, Shizuoka 410-0022, Japan
Opto-Electronic Advances, 25 March 2023
Abstract

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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