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Y-Net: A one-to-two deep learning framework for digital holographic reconstruction

  • Kaiqiang Wang
  • , Jiazhen Dou
  • , Qian Kemao
  • , Jianglei Di
  • , Jianlin Zhao
  • Northwestern Polytechnical University Xian
  • Nanyang Technological University

Research output: Contribution to journalArticlepeer-review

230 Scopus citations

Abstract

In this Letter, for the first time, to the best of our knowledge, we propose a digital holographic reconstruction method with a one-to-two deep learning framework (Y-Net). Perfectly fitting the holographic reconstruction process, the Y-Net can simultaneously reconstruct intensity and phase information from a single digital hologram. As a result, this compact network with reduced parameters brings higher performance than typical network variants. The experimental results of the mouse phagocytes demonstrate the advantages of the proposed Y-Net.

Original languageEnglish
Pages (from-to)4765-4768
Number of pages4
JournalOptics Letters
Volume44
Issue number19
DOIs
StatePublished - 1 Oct 2019

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