Evidence map›Paper›PMID 32998348›Full record

ArticleSensors (Basel, Switzerland)2020

Resolution Improvement of Light Field Imaging via a Nematic Liquid Crystal Microlens with Added Multi-Walled Carbon Nanotubes.

Hui Li, Yi Yu, Jing Peng, Yuntao Wu, Yanduo Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

5 authors.

Hui LiSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Yi YuSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Jing PengSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Yuntao WuSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Yanduo ZhangSchool of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Funding

China Postdoctoral Science Foundation 2014M562017Hubei Provincial Department of Education China D20171504Hubei Provincial Key Laboratory of Intelligent Robot HBIR 201805, HBIR 201901National Natural Science Foundation of China 51703071, 61771353Natural Science Foundation of Hubei Province 2019CFB553
6 · The paper itself

Abstract

A relatively simple method to improve the image resolution of light field based on a liquid crystal (LC) microlens doped with multi-walled carbon nanotubes (MWCNTs) was developed and evaluated. As the nanoparticles were doped in LC, its electro-optical features could enhance, leading to a short response time compared to the pure LC microlens. With the maximum use of the proposed LC microlens, a method combining aperiodicity extraction and weighted average algorithm was adopted to realize the high-resolution light field imaging. The aperiodicity extraction method was proposed, which could effectively improve resolution of view angle image. For synthesizing the full resolution image at 0 Vrms and the extracted view angle image of light field imaging at 2.0 Vrms, the final high-resolution light field imaging could be obtained in a short time by weighted average algorithm. In this way, the common problem of low resolution in light field imaging could be solved. This proposed method was in good agreement with our experimental results. And it was also in line with the development of the trend of the smart imaging sensor combining algorithm with hardware.

Indexed as

carbon nanotubesdopinglight field imagingliquid crystal microlens

Identifiers

PMID32998348
PMCPMC7582984

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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.