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Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning
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- Journal:
- Microscopy and Microanalysis / Volume 28 / Issue 1 / February 2022
- Published online by Cambridge University Press:
- 10 December 2021, pp. 138-144
- Print publication:
- February 2022
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- Article
- Export citation
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Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately
$5$ 
$e^{-}$ per pixel becomes comparable to that of images acquired with a total dose of approximately 
$1{,}000$ 
$e^{-}$ per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.
