Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

Yichen Wu; Yilin Luo; Gunvant Chaudhari; Yair Rivenson; Ayfer Calis; Kevin de Haan; Aydogan Ozcan

doi:10.1038/s41377-019-0139-9

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Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

Letters

- Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram
- Light: Science & Applications Vol. 8, Issue 2, Pages: 219-225(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 Nano Systems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
  4.David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-019-0139-9
  CLC：
- Published：2019，
  
  Published Online：06 March 2019，
  
  Received：10 December 2018，
  
  Revised：15 February 2019，
  
  Accepted：15 February 2019
- Accepted：
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Wu, Y. C. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light: Science & Applications, 8, 219-225 (2019).
DOI：

Wu, Y. C. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light: Science & Applications, 8, 219-225 (2019). DOI： 10.1038/s41377-019-0139-9.

摘要

Abstract

Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However

unlike a conventional bright-field microscopy image

the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here

we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast

combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this "bright-field holography" method through the snapshot imaging of bioaerosols distributed in 3D

matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging

and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram

benefiting from the wave-propagation framework of holography.

关键词

Keywords

references

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