Hao JY, Lin X, Lin YK, Chen MY, Chen RX et al. Lensless complex amplitude demodulation based on deep learning in holographic data storage. Opto-Electron Adv 6, 220157 (2023). doi: 10.29026/oea.2023.220157
Citation: Hao JY, Lin X, Lin YK, Chen MY, Chen RX et al. Lensless complex amplitude demodulation based on deep learning in holographic data storage. Opto-Electron Adv 6, 220157 (2023). doi: 10.29026/oea.2023.220157

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Lensless complex amplitude demodulation based on deep learning in holographic data storage

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  • 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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  • [1] Reinsel D, Gantz J, Rydning J. The Digitization of the World from Edge to Core (International Data Corporation, Framingham, 2018).

    Google Scholar

    [2] Flexible, scalable and reliable storage solution. Panasonic Connect. https://panasonic.net/cns/archiver/concept/

    Google Scholar

    [3] Anderson P, Black R, Cerkauskaite A, Chatzieleftheriou A, Clegg J et al. Glass: a new media for a new era?. In Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 2018) (USENIX Association, 2018).

    Google Scholar

    [4] Zhang JY, Gecevičius M, Beresna M, Kazansky PG. Seemingly unlimited lifetime data storage in nanostructured glass. Phys Rev Lett 112, 033901 (2014). doi: 10.1103/PhysRevLett.112.033901

    CrossRef Google Scholar

    [5] Dhar L, Curtis K, Fäcke T. Coming of age. Nat Photonics 2, 403–405 (2008). doi: 10.1038/nphoton.2008.120

    CrossRef Google Scholar

    [6] Lin X, Liu JP, Hao JY, Wang K, Zhang YY et al. Collinear holographic data storage technologies. Opto-Electron Adv 3, 190004 (2020). doi: 10.29026/oea.2020.190004

    CrossRef Google Scholar

    [7] Horimai H, Tan XD, Li J. Collinear holography. Appl Opt 44, 2575–2579 (2005). doi: 10.1364/AO.44.002575

    CrossRef Google Scholar

    [8] Project HSD: holographic storage device for the cloud. Microsoft. https://www.microsoft.com/en-us/research/project/hsd/

    Google Scholar

    [9] Liu JP, Zhang L, Wu AN, Tanaka Y, Shigaki M et al. High noise margin decoding of holographic data page based on compressed sensing. Opt Express 28, 7139–7151 (2020). doi: 10.1364/OE.386953

    CrossRef Google Scholar

    [10] Katano Y, Muroi T, Kinoshita N, Ishii N, Hayashi N. Data demodulation using convolutional neural networks for holographic data storage. Jpn J Appl Phys 57, 09SC01 (2018). doi: 10.7567/JJAP.57.09SC01

    CrossRef Google Scholar

    [11] Shimobaba T, Kuwata N, Homma M, Takahashi T, Nagahama Y et al. Convolutional neural network-based data page classification for holographic memory. Appl Opt 56, 7327–7330 (2017). doi: 10.1364/AO.56.007327

    CrossRef Google Scholar

    [12] Lin X, Huang Y, Shimura T, Fujimura R, Tanaka Y et al. Fast non-interferometric iterative phase retrieval for holographic data storage. Opt Express 25, 30905–30915 (2017). doi: 10.1364/OE.25.030905

    CrossRef Google Scholar

    [13] Hao JY, Wang K, Zhang YY, Li H, Lin X et al. Collinear non-interferometric phase retrieval for holographic data storage. Opt Express 28, 25795–25805 (2020). doi: 10.1364/OE.400599

    CrossRef Google Scholar

    [14] Lin X, Hao JY, Wang K, Zhang YY, Li H et al. Frequency expanded non-interferometric phase retrieval for holographic data storage. Opt Express 28, 511–518 (2020). doi: 10.1364/OE.380365

    CrossRef Google Scholar

    [15] Lin X, Huang Y, Li Y, Liu JY, Liu JP et al. Four-level phase pair encoding and decoding with single interferometric phase retrieval for holographic data storage. Chin Opt Lett 16, 032101 (2018). doi: 10.3788/COL201816.032101

    CrossRef Google Scholar

    [16] Nobukawa T, Nomura T. Multilevel recording of complex amplitude data pages in a holographic data storage system using digital holography. Opt Express 24, 21001–21011 (2016). doi: 10.1364/OE.24.021001

    CrossRef Google Scholar

    [17] Katano Y, Nobukawa T, Muroi T, Kinoshita N, Ishii N. CNN-based demodulation for a complex amplitude modulation code in holographic data storage. Opt Rev 28, 662–672 (2021). doi: 10.1007/s10043-021-00687-z

    CrossRef Google Scholar

    [18] Bunsen M, Tateyama S. Detection method for the complex amplitude of a signal beam with intensity and phase modulation using the transport of intensity equation for holographic data storage. Opt Express 27, 24029–24042 (2019). doi: 10.1364/OE.27.024029

    CrossRef Google Scholar

    [19] Chen RX, Hao JY, Yu CY, Zheng QJ, Qiu XY et al. Dynamic sampling iterative phase retrieval for holographic data storage. Opt Express 29, 6726–6736 (2021). doi: 10.1364/OE.419630

    CrossRef Google Scholar

    [20] Horisaki R, Fujii K, Tanida J. Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning. Opt Rev 25, 593–597 (2018). doi: 10.1007/s10043-018-0452-1

    CrossRef Google Scholar

    [21] Zuo HR, Xu ZY, Zhang JL, Jia G. Visual tracking based on transfer learning of deep salience information. Opto-Electron Adv 3, 190018 (2020). doi: 10.29026/oea.2020.190018

    CrossRef Google Scholar

    [22] Liao MH, Zheng SS, Pan SX, Lu DJ, He WQ et al. Deep-learning-based ciphertext-only attack on optical double random phase encryption. Opto-Electron Adv 4, 200016 (2021). doi: 10.29026/oea.2021.200016

    CrossRef Google Scholar

    [23] Liao K, Chen Y, Yu ZC, Hu XY, Wang XY et al. All-optical computing based on convolutional neural networks. Opto-Electron Adv 4, 200060 (2021). doi: 10.29026/oea.2021.200060

    CrossRef Google Scholar

    [24] Sinha A, Lee J, Li S, Barbastathis G. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017). doi: 10.1364/OPTICA.4.001117

    CrossRef Google Scholar

    [25] Wang H, Lyu M, Situ G. eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction. Opt Express 26, 22603–22614 (2018). doi: 10.1364/OE.26.022603

    CrossRef Google Scholar

    [26] Wang KQ, Dou JZ, Kemao Q, Di JL, Zhao JL. Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. Opt Lett 44, 4765–4768 (2019). doi: 10.1364/OL.44.004765

    CrossRef Google Scholar

    [27] Wang KQ, Kemao Q, Di JL, Zhao JL. Y4-Net: a deep learning solution to one-shot dual-wavelength digital holographic reconstruction. Opt Lett 45, 4220–4223 (2020). doi: 10.1364/OL.395445

    CrossRef Google Scholar

    [28] Wang F, Bian YM, Wang HC, Lyu M, Pedrini G et al. Phase imaging with an untrained neural network. Light Sci Appl 9, 77 (2020). doi: 10.1038/s41377-020-0302-3

    CrossRef Google Scholar

    [29] Situ G. Deep holography. Light Adv Manuf 3, 278–300 (2022). doi: 10.37188/lam.2022.013

    CrossRef Google Scholar

    [30] Hao JY, Lin X, Lin YK, Song HY, Chen RX et al. Lensless phase retrieval based on deep learning used in holographic data storage. Opt Lett 46, 4168–4171 (2021). doi: 10.1364/OL.433955

    CrossRef Google Scholar

    [31] Goodman JW. Introduction to Fourier Optics 2nd ed (McGraw-Hill, Singapore, 1996).

    Google Scholar

    [32] Tokoro M, Fujimura R. Single-shot detection of four-level phase modulated signals using inter-pixel crosstalk for holographic data storage. Jpn J Appl Phys 60, 022004 (2021). doi: 10.35848/1347-4065/abd86b

    CrossRef Google Scholar

    [33] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, 2015); http://doi.org/10.1007/978-3-319-24574-4_28.

    Google Scholar

    [34] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 521, 436–444 (2015). doi: 10.1038/nature14539

    CrossRef Google Scholar

    [35] Ferguson TS. An inconsistent maximum likelihood estimate. J Am Stat Assoc 77, 831–834 (1982). doi: 10.1080/01621459.1982.10477894

    CrossRef Google Scholar

    [36] Kingma DP, Ba J. Adam: a method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (2015).

    Google Scholar

    [37] Korhonen J, You JY. Peak signal-to-noise ratio revisited: is simple beautiful?. In Proceedings of the Fourth International Workshop on Quality of Multimedia Experience 37–38 (IEEE, 2012); http://doi.org/10.1109/QoMEX.2012.6263880.

    Google Scholar

    [38] Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13, 600–612 (2004). doi: 10.1109/TIP.2003.819861

    CrossRef Google Scholar

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