Evidence map›Paper›PMID 31645929›Full record

ArticleLight, science & applications2019

Deep learning in holography and coherent imaging.

Yair Rivenson, Yichen Wu, Aydogan Ozcan

Abstract read
In one paragraph

Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Article
  2. Beam manipulation for terahertz communications.Communications engineering · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. On the use of deep learning for phase recovery.Light, science & applications · 2024
    Review
  19. Article
  20. Review
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

3 authors.

Yair Rivenson *1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
Yichen Wu *1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.ORCID 0000-0002-9343-5489
Aydogan Ozcan1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.ORCID 0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.

Indexed as

Imaging and sensingMicroscopyPhase-contrast microscopy

Identifiers

PMID31645929
PMCPMC6804620

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.