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Deep learning for hologram generation
- 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.
- Subjects :
- Artificial neural network
Computer science
business.industry
Deep learning
Holography
Object (computer science)
Autoencoder
Atomic and Molecular Physics, and Optics
law.invention
Generative model
Optics
4009 Electronics, Sensors and Digital Hardware
5102 Atomic, Molecular and Optical Physics
law
Holographic display
Feature (machine learning)
Computer vision
Artificial intelligence
business
51 Physical Sciences
4006 Communications Engineering
ComputingMethodologies_COMPUTERGRAPHICS
40 Engineering
Subjects
Details
- ISSN :
- 10944087
- Volume :
- 29
- Issue :
- 17
- Database :
- OpenAIRE
- Journal :
- Optics express
- Accession number :
- edsair.doi.dedup.....b3c669d4bf7cec22972b239318c86142