Enhancing Electron Computational Ghost Imaging Using Artificial Neural Networks
Publication date: 22 Lug 2022
The observation of beam sensitive materials is a challenging field that benefits from the use of low electron doses and fast acquisition, to obtain as much information as possible before damaging the sample. Following this guiding principle, it is possible to borrow a technique first developed for light microscopy and adapt it to transmission electron microscopy.