Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

ABSTRACT: Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well known, STEM's image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM's current temporal resolution is far below the conventional TEM's. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized

SUBMITTER: Ihara S 

PROVIDER: S-EPMC9356044 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature