English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study a machine learning (ML) technique for refining images acquiredduring in situ observation using liquid-cell transmission electron microscopy(LC-TEM). Our model is constructed using a U-Net architecture and a ResNetencoder. For training our ML model, we prepared an original image dataset thatcontained pairs of images of samples acquired with and without a solutionpresent. The former images were used as noisy images and the latter images wereused as corresponding ground truth images. The number of pairs of image setswas $1,204$ and the image sets included images acquired at several differentmagnifications and electron doses. The trained model converted a noisy imageinto a clear image. The time necessary for the conversion was on the order of10ms, and we applied the model to in situ observations using the software GatanDigitalMicrograph (DM). Even if a nanoparticle was not visible in a view windowin the DM software because of the low electron dose, it was visible in asuccessive refined image generated by our ML model.

Machine learning refinement of in situ images acquired by low electron dose LC-TEM