Deep learning in holography

Byoungho Lee; Juhyun Lee; Dongheon Yoo; Eunbi Lee

doi:10.1117/12.2582645

5 March 2021 Deep learning in holography

Byoungho Lee, Juhyun Lee, Dongheon Yoo, Eunbi Lee

Proceedings Volume 11703, AI and Optical Data Sciences II; 117030W (2021) https://doi.org/10.1117/12.2582645
Event: SPIE OPTO, 2021, Online Only

Abstract

Holographic displays are considered to be promising technologies for augmented and virtual reality devices. Using spatial light modulators (SLMs), they can directly modulate the wavefront of light. Through the modulation of the wavefront, they can provide observers three-dimensional imagery. However, they suffer from a large computation load, and it is important to overcome the disadvantage for the popularization of holographic display techniques. In this invited paper, we adopt a deep learning algorithm for the fast generation of computer-generated holograms (CGHs). We propose the deep neural network designed for the generation of complex holograms. The overall algorithm for the learning-based generation of CGHs using the network is introduced, and the training strategy is provided. The simulation and experimental results are demonstrated, and we verified the feasibility of using the deep learning algorithm for CGH computation.

Conference Presentation

Citation Download Citation

Byoungho Lee, Juhyun Lee, Dongheon Yoo, and Eunbi Lee "Deep learning in holography", Proc. SPIE 11703, AI and Optical Data Sciences II, 117030W (5 March 2021); https://doi.org/10.1117/12.2582645

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available