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)

Article  Google Scholar 

Kurokawa, S., Yoshida, S.: Demodulation scheme for constant-weight codes using convolutional neural network in holographic data storage. Opt. Rev. 29, 375–381 (2022)

Article  Google Scholar 

Zhao, Y., Wu, F., Lin, X., Zhang, M., Yu, Q., Tan, X., Xie, C.: Phase-distribution-aware adaptive decision scheme to improve the reliability of holographic data storage. Opt. Express 30, 16655–16668 (2022)

Article  PubMed  ADS  Google Scholar 

Bunsen, M., Umetsu, S., Takabayashi, M., Okamoto, A.: Method of phase and amplitude modulation/demodulation using datapages with embedded phase-shift for holographic data storage. Jpn. J. Appl. Phys. 52, 09LD04 (2013)

Article  Google Scholar 

Katano, Y., Muroi, T., Kinoshita, N., Ishii, N.: Highly efficient dual page reproduction in holographic data storage. Opt. Express 29, 33257–33268 (2021)

Article  PubMed  ADS  Google Scholar 

Hao, J., Lin, X., Lin, Y., Song, H., Chen, R., Chen, M., Wang, K., Tan, X.: Lensless phase retrieval based on deep learning used in holographic data storage. Opt. Lett. 46, 4168–4171 (2021)

Article  PubMed  ADS  Google Scholar 

Mok, F.: Angle-multiplexed storage of 5000 holograms in lithium niobate. Opt. Lett. 18, 915–917 (1993)

Article  CAS  PubMed  ADS  Google Scholar 

Rakuljic, G.A., Layva, V., Yariv, A.: Optical data storage by using orthogonal wavelength-multiplexed volume holograms. Opt. Lett. 17(20), 1471–1473 (1992)

Article  CAS  PubMed  ADS  Google Scholar 

Horimai, H., Tan, X.D., Li, J.: Collinear holography. Appl. Opt. 44, 2575–2579 (2005)

Article  PubMed  ADS  Google Scholar 

Jia, W., Chen, Z., Wen, F.J., Zhou, C., Chow, Y.T., Chung, P.S.: Coaxial holographic encoding based on pure phase modulation. Appl. Opt. 50, H10–H15 (2011)

Article  PubMed  Google Scholar 

Tanaka, K., Hara, M., Tokuyama, K., Hirooka, K., Ishioka, K., Fukumoto, A., Watanabe, K.: Improved performance in coaxial holographic data recording. Opt. Express 15, 16196–16209 (2007)

Article  PubMed  ADS  Google Scholar 

Qiu, X., Wang, K., Lin, X., Hao, J., Lin, D., Zheng, Q., Chen, R., Wang, S., Tan, X.: Combination compensation method to improve the tolerance of recording medium shrinkage in collinear holographic storage. Photonics 9(3), 149 (2022)

Article  Google Scholar 

Takabayashi, M., Okamoto, A.: Self-referential holography and its applications to data storage and phase-to-intensity conversion. Opt. Express 21(3), 3669–3681 (2013)

Article  PubMed  ADS  Google Scholar 

Takabayashi, M., Okamoto, A., Eto, T., Okamoto, T.: Shift-multiplexed self-referential holographic data storage. Appl. Opt. 53(20), 4375–4381 (2014)

Article  PubMed  ADS  Google Scholar 

Takabayashi, M., Okamoto, A., Eto, T., Okamoto, T.: Recording procedures for high-quality signal readout in self-referential holographic data storage. Appl. Opt. 54(16), 5167–5174 (2015)

Article  PubMed  ADS  Google Scholar 

Eto, T., Takabayashi, M., Okamoto, A., Bunsen, M., Okamoto, T.: Numerical simulations on inter-page crosstalk characteristics in three-dimensional shift multiplexed self-referential holographic data storage. Jpn. J. Appl. Phys. 55(8), 08RD01 (2016)

Article  Google Scholar 

Takabayashi, M., Eto, T., Okamoto, T.: Numerical simulations on the focus-shift multiplexing technique for self-referential holographic data storage. Opt. Rev. 23(6), 987–996 (2016)

Article  CAS  Google Scholar 

Tomioka, R., Takabayashi, M.: Numerical simulations on optoelectronic deep neural network hardware based on self‑referential holography. Opt. Rev. 30, 387–396 (2023)

Article  Google Scholar 

Saita, Y., Nomura, T.: Design method of input phase mask to improve light use efficiency and reconstructed image quality for holographic memory. Appl. Opt. 53, 4136–4140 (2014)

Article  PubMed  ADS  Google Scholar 

Chijiwa, K., Takabayashi, M.: Fast designing method of additional patterns in self-referential holographic data storage-approach using deep neural network-. ITE Tech. Rep. 47, 35–40 (2023). (in Japanese)

Google Scholar 

Ronneberger, O., Philipp, F., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015)

Kang, S., Uchida, S., Iwana, B.K.: Tunable U-Net: controlling image-to-image outputs using a tunable scalar value. IEEE Access 9, 103279–103290 (2021)

Article  Google Scholar 

Laxman, K., Dubey, S.R., Kalyan, B., Kojjarapu, S.R.V.: Efficient high-resolution image-to-image translation using multi-scale gradient U-net. In: International Conference on Computer Vision and Image Processing. Springer International Publishing, Cham (2021)

Ohyama, W., Suzuki, M., Uchida, S.: Detecting mathematical expressions in scientific document images using a u-net trained on a diverse dataset. IEEE Access 7, 144030–144042 (2019)

Article  Google Scholar 

Ibtehaz, N., Sohel Rahman, M.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)

Article  PubMed  Google Scholar 

Tanaka, J., Okamoto, A., Kitano, M.: Development of image-based simulation for holographic data storage system by fast Fourier transform beam propagation method. Jpn. J. Appl. Phys. 48, 03A028 (2009)

Article  Google Scholar