Intensity and phase imaging through scattering media via deep despeckle complex neural networks

Shuai Liu, Peng Li, Hao Sha, Jiuyang Dong, Yue Huang, Yanjing Zhao, Xuri Yao, Qin Peng, Xiu Li, Xing Lin*, Yongbing Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The existence of a scattering medium causes the degeneration of intensity and phase information, especially in biological imaging. The present techniques to address this challenge only focus on the reconstruction of intensity information, yet few attempts have tried to recover the phase information. We propose a method to simultaneously predict both intensity and phase information from a speckle image employing a deep despeckle complex neural network (DespeckleNet). By combining the advantages of both the complex network and the generative adversarial network framework, our method enables the high contrast single-shot imaging of complicated biological samples through scattering media without labeling. Various experiments demonstrate the superior reconstruction and generalization performance of our method under multiple types of biological samples with different scattering media. We also provide the real-time observation of living cellular activities without any contaminations or damages to the cells. Our method offers simple yet effective imaging through scattering media and paves the way for real-time unlabeled biological imaging.

Original languageEnglish
Article number107196
JournalOptics and Lasers in Engineering
Volume159
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Despeckle
  • DespeckleNet
  • Quantitative phase imaging

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