Calculating real-time phase-only holograms through autoencoder neural network

Jiachen Wu; Kexuan Liu; Liangcai Cao

doi:10.1117/12.2579244

5 March 2021 Calculating real-time phase-only holograms through autoencoder neural network

Jiachen Wu, Kexuan Liu, Liangcai Cao

Proceedings Volume 11708, Advances in Display Technologies XI; 1170808 (2021) https://doi.org/10.1117/12.2579244
Event: SPIE OPTO, 2021, Online Only

Abstract

An autoencoder neural network is proposed for real-time phase-only CGH generation. As an unsupervised learning method, the input and output of the autoencoder are both the original images, which dispenses with calculating corresponding holograms. It could automatically learn the encoding of phase-only holograms during the training period. Once the training is completed, the phase-only hologram of any two-dimensional image can be quickly generated. The calculation time is 1-2 orders of magnitude faster than the traditional iterative algorithms and the reconstructed image quality is improved.

Conference Presentation

Citation Download Citation

Jiachen Wu, Kexuan Liu, and Liangcai Cao "Calculating real-time phase-only holograms through autoencoder neural network", Proc. SPIE 11708, Advances in Display Technologies XI, 1170808 (5 March 2021); https://doi.org/10.1117/12.2579244

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