ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Photorealistic 3D Holographic Display with Natural Defocus Effect.Nature communications · 2026Article
- Propagation-adaptive 4K computer-generated holography using physics-constrained spatial and Fourier neural operator.Nature communications · 2025Article
- Review
- Generation of Multiple-Depth 3D Computer-Generated Holograms from 2D-Image-Datasets Trained CNN.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (L
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.