Evidence map›Paper›PMID 36698670›Full record

ArticleBiomedical optics express2023

Surpassing the resolution limitation of structured illumination microscopy by an untrained neural network.

Yu He, Yunhua Yao, Yilin He, Zhengqi Huang, Fan Luo, Chonglei Zhang, Dalong Qi, Tianqing Jia, Zhiyong Wang, Zhenrong Sun and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2023. 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. Artificial Neural Networks for Bioimage Analysis.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  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

12 authors.

Yu HeState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Yunhua YaoState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.ORCID https://orcid.org/0000-0002-1894-1867
Yilin HeState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Zhengqi HuangState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Fan LuoNanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China.
Chonglei ZhangNanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China.ORCID https://orcid.org/0000-0002-9259-8972
Dalong QiState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Tianqing JiaState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Zhiyong WangSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Zhenrong SunState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Xiaocong YuanNanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China.
Shian ZhangState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.ORCID https://orcid.org/0000-0003-3168-4962

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structured illumination microscopy (SIM), as a flexible tool, has been widely applied to observing subcellular dynamics in live cells. It is noted, however, that SIM still encounters a problem with theoretical resolution limitation being only twice over wide-field microscopy, where imaging of finer biological structures and dynamics are significantly constrained. To surpass the resolution limitation of SIM, we developed an image postprocessing method to further improve the lateral resolution of SIM by an untrained neural network, i.e., deep resolution-enhanced SIM (DRE-SIM). DRE-SIM can further extend the spatial frequency components of SIM by employing the implicit priors based on the neural network without training datasets. The further super-resolution capability of DRE-SIM is verified by theoretical simulations as well as experimental measurements. Our experimental results show that DRE-SIM can achieve the resolution enhancement by a factor of about 1.4 compared with conventional SIM. Given the advantages of improving the lateral resolution while keeping the imaging speed, DRE-SIM will have a wide range of applications in biomedical imaging, especially when high-speed imaging mechanisms are integrated into the conventional SIM system.

Identifiers

PMID36698670
PMCPMC9842007

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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.