Evidence map›Paper›PMID 33149965›Full record

ArticleBiomedical optics express2020

Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells.

Zhiduo Zhang, Yujie Zheng, Tienan Xu, Avinash Upadhya, Yean Jin Lim, Alexander Mathews, Lexing Xie, Woei Ming Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Interferometric image denoising network SEVReNet.Biomedical optics express · 2026
    Article
  2. Article
  3. Article
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  5. 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

8 authors.

Zhiduo ZhangResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.
Yujie ZhengResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.
Tienan XuResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.
Avinash UpadhyaResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.ORCID https://orcid.org/0000-0003-0841-303X
Yean Jin LimResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.
Alexander MathewsResearch School of Computer Science, College of Engineering and Computer Science, The Australian National University, 108 North Road, Canberra, ACT 2601, Australia.
Lexing XieResearch School of Computer Science, College of Engineering and Computer Science, The Australian National University, 108 North Road, Canberra, ACT 2601, Australia.
Woei Ming LeeResearch School of Electrical, Energy and Materials Engineering, College of Engineering and Computer Science, The Australian National University, 31 North Road, Canberra, ACT 2601, Australia.ORCID https://orcid.org/0000-0002-3912-6095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm

Identifiers

PMID33149965
PMCPMC7587275

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.