DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning

Yuanlong Zhang; Bo Xiong; Yi Zhang; Zhi Lu; Jiamin Wu; Qionghai Dai

doi:10.1038/s41377-021-00587-6

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DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning

Articles

- DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning
- Light: Science & Applications Vol. 10, Issue 8, Pages: 1546-1557(2021)
- Affiliations：
  
  1.Department of Automation, Tsinghua University, Beijing, 100084, China
  2.Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, 100084, China
  3.Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China
- Author bio：
  
  Jiamin Wu (wujiamin@tsinghua.edu.cn)
  Qionghai Dai (qhdai@mail.tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-021-00587-6
  CLC：
- Published：31 August 2021，
  
  Published Online：27 July 2021，
  
  Received：05 December 2020，
  
  Revised：04 June 2021，
  
  Accepted：02 July 2021
- Accepted：
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Zhang, Y. L. et al. DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning. Light: Science & Applications, 10, 1546-1557 (2021).
DOI：

Zhang, Y. L. et al. DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning. Light: Science & Applications, 10, 1546-1557 (2021). DOI： 10.1038/s41377-021-00587-6.

摘要

Abstract

Light field microscopy (LFM) has been widely used for recording 3D biological dynamics at camera frame rate. However

LFM suffers from artifact contaminations due to t

he illness of the reconstruction problem via naïve Richardson–Lucy (RL) deconvolution. Moreover

the performance of LFM significantly dropped in low-light conditions due to the absence of sample priors. In this paper

we thoroughly analyze different kinds of artifacts and present a new LFM technique termed dictionary LFM (DiLFM) that substantially suppresses various kinds of reconstruction artifacts and improves the noise robustness with an over-complete dictionary. We demonstrate artifact-suppressed reconstructions in scattering samples such as

Drosophila

embryos and brains. Furthermore

we show our DiLFM can achieve robust blood cell counting in noisy conditions by imaging blood cell dynamic at 100 Hz and unveil more neurons in whole-brain calcium recording of zebrafish with low illumination power in vivo.

关键词

Keywords

references

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