Fast improvement of TEM image with low-dose electrons by deep learning
This enables in situ observation of electron-beam-sensitive specimens in materials science and biology, representing a strong specific gain rather than a broad paradigm shift.
The researchers tackled the problem of low-dose electron microscopy imaging by applying a convolutional neural network to improve short-exposure TEM images, achieving image quality comparable to a 200-fold higher dose with a conversion time of 8 ms enabling 125 fps in situ observation.
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.