Evidence map›Paper›PMID 38424072›Full record

ArticleLight, science & applications2024

Liquid lens based holographic camera for real 3D scene hologram acquisition using end-to-end physical model-driven network.

Di Wang, Zhao-Song Li, Yi Zheng, You-Ran Zhao, Chao Liu, Jin-Bo Xu, Yi-Wei Zheng, Qian Huang, Chen-Liang Chang, Da-Wei Zhang and 2 more

Abstract read
In one paragraph

Article in Light, science & applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Di Wang *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Zhao-Song Li *School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Yi ZhengSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
You-Ran ZhaoSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Chao LiuSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Jin-Bo XuSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Yi-Wei ZhengSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Qian HuangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
Chen-Liang ChangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Da-Wei ZhangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.ORCID http://orcid.org/0000-0002-0841-7826
Song-Lin ZhuangSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Qiong-Hua WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China. qionghua@buaa.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62020106010, 62275009, U22A2079, U21B2034
6 · The paper itself

Abstract

With the development of artificial intelligence, neural network provides unique opportunities for holography, such as high fidelity and dynamic calculation. How to obtain real 3D scene and generate high fidelity hologram in real time is an urgent problem. Here, we propose a liquid lens based holographic camera for real 3D scene hologram acquisition using an end-to-end physical model-driven network (EEPMD-Net). As the core component of the liquid camera, the first 10 mm large aperture electrowetting-based liquid lens is proposed by using specially fabricated solution. The design of the liquid camera ensures that the multi-layers of the real 3D scene can be obtained quickly and with great imaging performance. The EEPMD-Net takes the information of real 3D scene as the input, and uses two new structures of encoder and decoder networks to realize low-noise phase generation. By comparing the intensity information between the reconstructed image after depth fusion and the target scene, the composite loss function is constructed for phase optimization, and the high-fidelity training of hologram with true depth of the 3D scene is realized for the first time. The holographic camera achieves the high-fidelity and fast generation of the hologram of the real 3D scene, and the reconstructed experiment proves that the holographic image has the advantage of low noise. The proposed holographic camera is unique and can be used in 3D display, measurement, encryption and other fields.

Identifiers

PMID38424072
PMCPMC10904790

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Registered trials

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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.