Digital holograms can be calculated by simulating light wave propagation on a computer. Hologram calculations are used for three-dimensional displays. However, the calculations take a long time, and the data size of the calculated holograms becomes large. This study presents a deep-learning-assisted holo- gram calculation using low-sampling holograms. We calculate holograms with low-sampling rates, resulting in the acceleration of the hologram calculation and the decrease of the hologram size. However, the low-sampling holograms decrease the quality of the reconstructed images and will occur the aliasing errors when not satisfying the Nyquist rate. The proposed method uses a deep neural network to retrieve the full-sampling holograms from the low-sampling holograms. We show elementary results of the proposed method in numerical simulation.
Shimobaba, T, Blinder, D, Schelkens, P, Yamamoto, Y, Hoshi, I, Kakue, T & Ito, T 2020, Deep-learning-assisted hologram calculation via low-sampling holograms. in 8th International Congress on Advanced Applied Informatics - 4th International Conference on Enterprise Architecture and Information Systems (EAIS 2019). IEEE Computer Society, 8th International Congress on Advanced Applied Informatics, Toyama, Japan, 7/07/19.
Shimobaba, T., Blinder, D., Schelkens, P., Yamamoto, Y., Hoshi, I., Kakue, T., & Ito, T. (2020). Deep-learning-assisted hologram calculation via low-sampling holograms. In 8th International Congress on Advanced Applied Informatics - 4th International Conference on Enterprise Architecture and Information Systems (EAIS 2019) IEEE Computer Society.
@inproceedings{963a7c4187994c14b4b0a6da2b10c914,
title = "Deep-learning-assisted hologram calculation via low-sampling holograms",
abstract = "Digital holograms can be calculated by simulating light wave propagation on a computer. Hologram calculations are used for three-dimensional displays. However, the calculations take a long time, and the data size of the calculated holograms becomes large. This study presents a deep-learning-assisted holo- gram calculation using low-sampling holograms. We calculate holograms with low-sampling rates, resulting in the acceleration of the hologram calculation and the decrease of the hologram size. However, the low-sampling holograms decrease the quality of the reconstructed images and will occur the aliasing errors when not satisfying the Nyquist rate. The proposed method uses a deep neural network to retrieve the full-sampling holograms from the low-sampling holograms. We show elementary results of the proposed method in numerical simulation.",
keywords = "holography, deep learning, artificial intelligence, AI, Image coding",
author = "Tomoyoshi Shimobaba and David Blinder and Peter Schelkens and Yota Yamamoto and Ikuo Hoshi and Takashi Kakue and Tomoyoshi Ito",
year = "2020",
month = feb,
day = "13",
language = "English",
booktitle = "8th International Congress on Advanced Applied Informatics - 4th International Conference on Enterprise Architecture and Information Systems (EAIS 2019)",
publisher = "IEEE Computer Society",
address = "United States",
note = "8th International Congress on Advanced Applied Informatics : 4th International Conference on Enterprise Architecture and Information Systems, EAIS 2019 ; Conference date: 07-07-2019 Through 12-07-2019",
url = "http://www.iaiai.org/conference/aai2019/",
}