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Simulation-Trained Sparse Coding for High-Precision Phase Imaging in Low-Dose Electron Holography

Published online by Cambridge University Press:  09 June 2020

Satoshi Anada ,
Yuki Nomura ,
Tsukasa Hirayama  and
Kazuo Yamamoto
Satoshi Anada*
Affiliation:
Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
Yuki Nomura
Affiliation:
Technology Innovation Division, Panasonic Corporation, 3-1-1 Yagumo-Nakamachi, Moriguchi, Osaka570-8501, Japan
Tsukasa Hirayama
Affiliation:
Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
Kazuo Yamamoto
Affiliation:
Nanostructures Research Laboratory, Japan Fine Ceramics Center, 2-4-1 Mutsuno, Atsuta-ku, Nagoya, Aichi456-8587, Japan
*
*Author for correspondence: Satoshi Anada, E-mail: s_anada@jfcc.or.jp
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Abstract

We broaden the applicability of sparse coding, a machine learning method, to low-dose electron holography by using simulated holograms for learning and validation processes. The holograms, with shot noise, are prepared to generate a model, or a dictionary, that includes basic features representing interference fringes. The dictionary is applied to sparse representations of other simulated holograms with various signal-to-noise ratios (SNRs). Results demonstrate that this approach successfully removes noise for holograms with an extremely small SNR of 0.10, and that the denoised holograms provide the accurate phase distribution. Furthermore, this study demonstrates that the dictionary learned from the simulated holograms can be applied to denoising of experimental holograms of a p–n junction specimen recorded with different exposure times. The results indicate that the simulation-trained sparse coding is suitable for use over a wide range of imaging conditions, in particular for observing electron beam-sensitive materials.

Keywords

electron holographyhologram simulationimage denoisinglow-dose imagingmachine learningsparse coding
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 26 , Issue 3 , June 2020 , pp. 429 - 438
Copyright
Copyright © Microscopy Society of America 2020

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