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.

翻译：我们研究了一种基于机器学习（ML）的精炼技术，用于优化在液体池透射电子显微镜（LC-TEM）原位观测中获取的图像。该模型采用U-Net架构与ResNet编码器构建。为训练ML模型，我们制备了包含样品在有无溶液条件下成对图像的原创数据集：前者作为噪声图像，后者作为对应的真实参考图像。该数据集包含$1,204$对图像集，涵盖不同放大倍数和电子剂量下采集的影像。训练后的模型能将噪声图像转化为清晰图像，转化时间约为10毫秒量级，我们将其应用于Gatan DigitalMicrograph (DM)软件的原位观测中。即使因低电子剂量导致纳米颗粒在DM软件的观察窗口中不可见，经ML模型连续精炼生成的图像仍能使其清晰显现。