Deep convolutional neural network image processing method providing improved signal-To-noise ratios in electron holography

Yusuke Asari, Shohei Terada, Toshiaki Tanigaki, Yoshio Takahashi, Hiroyuki Shinada, Hiroshi Nakajima, Kiyoshi Kanie, Yasukazu Murakami

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

An image identification method was developed with the aid of a deep convolutional neural network (CNN) and applied to the analysis of inorganic particles using electron holography. Despite significant variation in the shapes of α-Fe2O3 particles that were observed by transmission electron microscopy, this CNN-based method could be used to identify isolated, spindle-shaped particles that were distinct from other particles that had undergone pairing and/or agglomeration. The averaging of images of these isolated particles provided a significant improvement in the phase analysis precision of the electron holography observations. This method is expected to be helpful in the analysis of weak electromagnetic fields generated by nanoparticles showing only small phase shifts.

Original languageEnglish
Pages (from-to)442-449
Number of pages8
JournalMicroscopy
Volume70
Issue number5
DOIs
Publication statusPublished - 2021 Oct 1

Keywords

  • convolutional neural network
  • electron holography
  • image processing
  • machine learning
  • nanoparticles
  • noise reduction

ASJC Scopus subject areas

  • Structural Biology
  • Instrumentation
  • Radiology Nuclear Medicine and imaging

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