ArticleNature communications2023
Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Computational optical sectioning in Fresnel incoherent correlation holography.Scientific reports · 2025Article
- Acoustic hologram-enabled simultaneous multi-target blood-brain barrier opening (AH-SiMBO).Communications engineering · 2025Article
- Review
- Spatial incoherence-driven optical reconstruction of holograms with observer shift-invariance.Light, science & applications · 2025Article
- Single-shot incoherent digital holography based on generalised three-step phase-shifting method with 1D phase grating.Scientific reports · 2025Article
- Real-time holographic camera for obtaining real 3D scene hologram.Light, science & applications · 2025Article
- Direct amplitude-only hologram realized by broken symmetry.Science advances · 2024Article
- Radiationless optical modes in metasurfaces: recent progress and applications.Light, science & applications · 2024Review
- Liquid lens based holographic camera for real 3D scene hologram acquisition using end-to-end physical model-driven network.Light, science & applications · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While recent research has shown that holographic displays can represent photorealistic 3D holograms in real time, the difficulty in acquiring high-quality real-world holograms has limited the realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they prevent the safety issues associated with the use of lasers; however, these cameras are hindered by severe noise due to the optical imperfections of such systems. In this work, we develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. A neural network filters the noise in the captured holograms, maintaining a complex-valued hologram format throughout the whole process. Enabled by the computational efficiency of the proposed filtering strategy, we demonstrate a holographic streaming system integrating a holographic camera and holographic display, with the aim of developing the ultimate holographic ecosystem of the future.
Identifiers
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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.