Machine learning refinement of in situ images acquired by low electron
  dose LC-TEM
Hiroyasu Katsuno Yuki Kimura Tomoya Yamazaki Ichigaku Takigawa
We study a machine learning (ML) technique for refining images acquired
during in situ observation using liquid-cell transmission electron microscopy
(LC-TEM). Our model is constructed using a U-Net architecture and a ResNet
encoder. For training our ML model, we prepared an original image dataset that
contained pairs of images of samples acquired with and without a solution
present. The former images were used as noisy images and the latter images were
used as corresponding ground truth images. The number of pairs of image sets
was $1,204$ and the image sets included images acquired at several different
magnifications and electron doses. The trained model converted a noisy image
into a clear image. The time necessary for the conversion was on the order of
10ms, and we applied the model to in situ observations using the software Gatan
DigitalMicrograph (DM). Even if a nanoparticle was not visible in a view window
in the DM software because of the low electron dose, it was visible in a
successive refined image generated by our ML model.