Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files
Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmis...
We present real-world data processing on measured electron time-of-flight data via neural networks.S...
In this study we explore the possibility to use deep learning for the reconstruction of phase images...
The key to optimizing spatial resolution in a state-of-the-art scanning transmission electron micros...
Dataset containing the jupyter notebook with codes for the simulation of the structured illumination...
This repository contains training data (HAADF images and FFT-HAADF descriptors, 80/20 splits employe...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
Source code for neural networks that supersample scanning transmission electron micrographs. This ca...
This work deals with the use of a convolutional neural network in the area of segmentation of images...
Longitudinal properties of electron bunches are critical for the performance of a wide range of scie...
Longitudinal beam diagnostics are a useful aid during tuning of particle accelerators, but acquiring...
Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize m...
This is the image dataset and model used to produce the results reported in the following publicatio...
We report the development of deep-learning coherent electron diffractive imaging at subangstrom reso...
Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmis...
We present real-world data processing on measured electron time-of-flight data via neural networks.S...
In this study we explore the possibility to use deep learning for the reconstruction of phase images...
The key to optimizing spatial resolution in a state-of-the-art scanning transmission electron micros...
Dataset containing the jupyter notebook with codes for the simulation of the structured illumination...
This repository contains training data (HAADF images and FFT-HAADF descriptors, 80/20 splits employe...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
Source code for neural networks that supersample scanning transmission electron micrographs. This ca...
This work deals with the use of a convolutional neural network in the area of segmentation of images...
Longitudinal properties of electron bunches are critical for the performance of a wide range of scie...
Longitudinal beam diagnostics are a useful aid during tuning of particle accelerators, but acquiring...
Transmission electron microscopy (TEM) is one of the most powerful techniques used to characterize m...
This is the image dataset and model used to produce the results reported in the following publicatio...
We report the development of deep-learning coherent electron diffractive imaging at subangstrom reso...
Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmis...
We present real-world data processing on measured electron time-of-flight data via neural networks.S...
In this study we explore the possibility to use deep learning for the reconstruction of phase images...