Fast improvement of TEM image with low-dose electrons by deep learning
Not on the map. Why?
Abstract (arXiv)
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 1000 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 3 Jun 2021.
Builds on
7 of 32 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
- 2018Learning to See in the Darkcited by 460
- 2018Getting to Know Low-light Images with The Exclusively Dark Datasetcited by 157
- 2020Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Imagescited by 10
Built on by
Citing papers per year: 2023: 1.
1 paper.