Advancements are reported in computer-generated holography proofing RGB 4K display through a new strategy based on diffraction model-driven deep networks. In the new 4K-DMDNet, the network is not a “black box” anymore. Rather, the input-output relation must obey to the physics of wavefront propagation, which is embedded here as a constraint. Thus, a labelled dataset is not required, and the model shows superior generalization capabilities with respect to data-driven approaches. The method is promising for the new generation of RGB 4K holographic display, as well as augmented and virtual reality systems.

Advancing computer-generated holographic display thanks to diffraction model-driven deep nets

Bianco V.;Ferraro P.
2024

Abstract

Advancements are reported in computer-generated holography proofing RGB 4K display through a new strategy based on diffraction model-driven deep networks. In the new 4K-DMDNet, the network is not a “black box” anymore. Rather, the input-output relation must obey to the physics of wavefront propagation, which is embedded here as a constraint. Thus, a labelled dataset is not required, and the model shows superior generalization capabilities with respect to data-driven approaches. The method is promising for the new generation of RGB 4K holographic display, as well as augmented and virtual reality systems.
2024
Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" - ISASI
Holographic display
Deep learning
Model-driven deep neural networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/523804
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