Enhancing Electron Computational Ghost Imaging Using Artificial Neural Networks

Publication date: 22 Lug 2022

JournalSource: OPENALEXOpenAlex type: articleOpen Access
Authors: Lorenzo Viani, Paolo Rosi, Enzo Rotunno, Stefano Frabboni, Roberto Balboni, Vincenzo Grillo

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.

Origin
Microscopy and Microanalysis
Volume
28
Issue
S1
Pages
2242-2244
Cited by
1