Denoising Low-dose Images Using Deep Learning of Time Series Images
Yang Shao Toshie Yaguchi Toshiaki Tanigaki
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.