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 , [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 { extcopyright} 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 " ,
}