Fast reconstruction of scanning transmission electron microscopy images using Markov random field model

Taichi Kusumi; Shun Katakami; Ryo Ishikawa; Kazuki Kawahara; Naoya Shibata; Masato Okada

doi:10.1016/j.ultramic.2023.113811

Fast reconstruction of scanning transmission electron microscopy images using Markov random field model

Ultramicroscopy. 2023 Nov:253:113811. doi: 10.1016/j.ultramic.2023.113811. Epub 2023 Jul 17.

Authors

Taichi Kusumi¹, Shun Katakami¹, Ryo Ishikawa², Kazuki Kawahara², Naoya Shibata³, Masato Okada⁴

Affiliations

¹ Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha 5-1-5, Chiba 277-8561, Kashiwa, Japan.
² Institute of Engineering Innovation, The University of Tokyo, Hongo 7-3-1, Tokyo 113-8656, Bunkyo, Japan.
³ Institute of Engineering Innovation, The University of Tokyo, Hongo 7-3-1, Tokyo 113-8656, Bunkyo, Japan; Nanostructures Research Laboratory, Japan Fine Ceramics Center, Atsuta Mutsuno 2-4-1, Aichi 456-8587, Nagoya, Japan.
⁴ Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha 5-1-5, Chiba 277-8561, Kashiwa, Japan. Electronic address: okada@edu.k.u-tokyo.ac.jp.

PMID: 37499573
DOI: 10.1016/j.ultramic.2023.113811

Abstract

In this study, we proposed a fast method of reconstruction for scanning transmission electron microscopy images. The proposed method is based on the Markov random field model and Bayesian inference, and we found that the method can reconstruct such images of sizes 512 × 512 and 264 × 240 in less than 200 ms and 100 ms, respectively. Furthermore, we showed that the method of reconstruction from multiple images without averaging them has better reconstruction performance than that from the averaged image.

Keywords: Bayesian inference; Denoising; Markov random field model; Scanning transmission electron microscopy.