
Deep learning for hologram generation
Author(s) -
ShengChi Liu,
Daping Chu
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.418803
Subject(s) - holography , autoencoder , computer science , artificial intelligence , generative model , feature (linguistics) , generative grammar , object (grammar) , deep learning , pattern recognition (psychology) , computer vision , optics , physics , linguistics , philosophy
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.