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Deep learning for hologram generation

Authors :
Sheng-Chi Liu
Daping Chu
Chu, Daping [0000-0001-9989-6238]
Apollo - University of Cambridge Repository
Source :
Optics express. 29(17)
Publication Year :
2021

Abstract

This work exploits deep learning to develop real-time hologram generation. We propose an original concept of introducing hologram modulators to allow the use of generative models to interpret complex-valued frequency data directly. This new mechanism enables the pre-trained learning model to generate frequency samples with variations in the underlying generative features. To achieve an object-based hologram generation, we also develop a new generative model, named the channeled variational autoencoder (CVAE). The pre-trained CVAE can then interpret and learn the hidden structure of input holograms. It is thus able to generate holograms through the learning of the disentangled latent representations, which can allow us to specify each disentangled feature for a specific object. Additionally, we propose a new technique called hologram super-resolution (HSR) to super-resolve a low-resolution hologram input to a super-resolution hologram output. Combining the proposed CVAE and HSR, we successfully develop a new approach to generate super-resolved, complex-amplitude holograms for 3D scenes.

Details

ISSN :
10944087
Volume :
29
Issue :
17
Database :
OpenAIRE
Journal :
Optics express
Accession number :
edsair.doi.dedup.....b3c669d4bf7cec22972b239318c86142