Deep learning in holography and coherent imaging

Yair Rivenson; Yichen Wu; Aydogan Ozcan

doi:10.1038/s41377-019-0196-0

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Deep learning in holography and coherent imaging

Perspectives

- Deep learning in holography and coherent imaging
- Light: Science & Applications Vol. 8, Issue 5, Pages: 661-668(2019)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
  4.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-019-0196-0
  CLC：
- Published：2019，
  
  Published Online：11 September 2019，
  
  Received：22 March 2019，
  
  Revised：18 August 2019，
  
  Accepted：18 August 2019
- Accepted：
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Rivenson, Y., Wu, Y. C. & Ozcan, A. Deep learning in holography and coherent imaging. Light: Science & Applications, 8, 661-668 (2019).
DOI：

Rivenson, Y., Wu, Y. C. & Ozcan, A. Deep learning in holography and coherent imaging. Light: Science & Applications, 8, 661-668 (2019). DOI： 10.1038/s41377-019-0196-0.

摘要

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.

关键词

Keywords

references

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