Add to ORKGSource code for neural networks that supersample scanning transmission electron micrographs. This can be used to decrease electron dose and scan time, to reduce damage to samples. It includes an implementation on an electron microscope that we tested on the Warwick ARM200F. There is also code to analyse data and create figures
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution stru...
71 pagesDirect electron detectors (DED) are enabling new materials imaging techniques such as four-d...
Low electron dose observation is indispensable for observing various samples using a transmission el...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for a neural network based on Xception that improves electron micrograph signal-to-noise...
Dataset containing the jupyter notebook used to construct the database of image, to model and train ...
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam e...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron mic...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
We review the growing use of machine learning in electron microscopy (EM) driven in part by the avai...
Scanning transmission electron microscopy (STEM) provides structural analysis with sub-angstrom reso...
The aim of this thesis is to create a deep neural net capable of super-resolution on images acquired...
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution stru...
71 pagesDirect electron detectors (DED) are enabling new materials imaging techniques such as four-d...
Low electron dose observation is indispensable for observing various samples using a transmission el...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for a neural network based on Xception that improves electron micrograph signal-to-noise...
Dataset containing the jupyter notebook used to construct the database of image, to model and train ...
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam e...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron mic...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
We review the growing use of machine learning in electron microscopy (EM) driven in part by the avai...
Scanning transmission electron microscopy (STEM) provides structural analysis with sub-angstrom reso...
The aim of this thesis is to create a deep neural net capable of super-resolution on images acquired...
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution stru...
71 pagesDirect electron detectors (DED) are enabling new materials imaging techniques such as four-d...
Low electron dose observation is indispensable for observing various samples using a transmission el...