Shimobaba, T., Blinder, D., Birnbaum, T., Hoshi, I., Shiomi, H., Schelkens, P., & Ito, T. (2022). Deep-Learning Computational Holography: A Review. Frontiers in Photonics, 3, Article 854391. https://doi.org/10.3389/fphot.2022.854391

@article{bb5e3cdb431f4c9b8ea3a2becac9bac4,
title = "Deep-Learning Computational Holography: A Review",
abstract = "Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.",
keywords = "computer-generated hologram, deep learning, digital holography, digital holographic microscopy, microscopy, neural network, three-dimensional display",
author = "Tomoyoshi Shimobaba and David Blinder and Tobias Birnbaum and Ikuo Hoshi and Harutaka Shiomi and Peter Schelkens and Tomoyoshi Ito",
note = "Publisher Copyright: Copyright {\textcopyright} 2022 Shimobaba, Blinder, Birnbaum, Hoshi, Shiomi, Schelkens and Ito.",
year = "2022",
month = mar,
day = "28",
doi = "10.3389/fphot.2022.854391",
language = "English",
volume = "3",
journal = "Frontiers in Photonics",
issn = "2673-6853",
publisher = "Frontiers Media",
}