Deep-Learning Computational Holography: A Review (Invited)

Tomoyoshi Shimobaba, David Blinder, Tobias Birnbaum, Ikuo Hoshi, Harutaka Shiomi, Peter Schelkens, Tomoyoshi Ito
2022 Frontiers in Photonics  
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.
doi:10.3389/fphot.2022.854391 fatcat:6yekzxerlvbj7bs4tkr6xuqufe