Digital image devices have been widely applied in many fields, including scientific imaging, recognition of individuals, and remote sensing. As the application of these imaging technologies to autonomous driving and measurement, image noise generated when observation cannot be performed with a sufficient dose has become a major problem. Machine learning denoise technology is expected to be the solver of this problem, but there are the following problems. Here we report, artifacts generated by machine learning denoise in ultra-low dose observation using an in-situ observation video of an electron microscope as an example. And as a method to solve this problem, we propose a method to decompose a time series image into a 2D image of the spatial axis and time to perform machine learning denoise. Our method opens new avenues accurate and stable reconstruction of continuous high-resolution images from low-dose imaging in science, industry, and life.

数字图像设备广泛应用于许多领域，但当观察不能使用足够剂量时会出现图像噪声问题。机器学习去噪技术被期望解决此问题，然而存在以下问题。本文以电子显微镜的原位观察视频为例，报告了机器学习去噪在超低剂量观察中产生的伪影问题，并提出了一种将时间序列图像分解为空间轴和时间的二维图像的方法进行机器学习去噪，从而实现了在科学、工业和生活中从低剂量成像中准确稳定地重建连续高分辨率图像的新途径。

利用时间序列图像的深度学习对低剂量图像进行去噪