Deep learning denoising diffusion probabilistic model applied to holographic data synthesis

In this Letter, we demonstrate for the first time, to our knowledge, a holographic data synthesis based on a deep learning probabilistic diffusion model (DDPM). Several different datasets of color images corresponding to different types of objects are converted to complex-valued holographic data thr...

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Veröffentlicht in:	Optics letters 2024-02, Vol.49 (3), p.514-517
Hauptverfasser:	Velez-Zea, Alejandro, Gutierrez-Cespedes, Cristian David, Barrera-Ramírez, John Fredy
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Back propagation networks Color imagery Datasets Deep learning Holograms Holography Probabilistic models Random noise Synthesis
Online-Zugang:	Volltext
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