# A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns.

@article{Xu2017ADC, title={A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns.}, author={W. Xu and J M Lebeau}, journal={Ultramicroscopy}, year={2017}, volume={188}, pages={ 59-69 } }

## 40 Citations

### Atomic Resolution Convergent Beam Electron Diffraction Analysis Using Convolutional Neural Networks

- Computer ScienceMicroscopy and Microanalysis
- 2019

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam…

### Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks.

- Computer ScienceUltramicroscopy
- 2019

### Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction datasets.

- Materials ScienceUltramicroscopy
- 2021

### Optimal STEM Convergence Angle Selection Using a Convolutional Neural Network and the Strehl Ratio

- Computer ScienceMicroscopy and Microanalysis
- 2020

It is shown that the Strehl ratio provides an accurate and efficient way to calculate criteria for evaluating the probe size for STEM and is shown to outperform experienced microscopists at selecting a convergence angle from a single electron Ronchigram using simulated datasets.

### Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks

- Computer ScienceMicroscopy and Microanalysis
- 2018

A deep convolutional neural network architecture is designed and implemented to predict crystal orientation from the EBSD patterns and a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth is designed.

### Deep Convolutional Neural Network Image Processing Method Providing Improved Signal-to-Noise Ratios in Electron Holography.

- PhysicsMicroscopy
- 2021

An image identification method was developed with the aid of a deep convolutional neural network and applied to the analysis of inorganic particles using electron holography to identify isolated, spindle-shaped particles that were distinct from other particles that had undergone pairing and/or agglomeration.

### Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder

- Computer ScienceUltramicroscopy
- 2019

### Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

- Physicsnpj Computational Materials
- 2021

The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic…

### TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

- Computer ScienceScientific reports
- 2021

Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method.

### TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images

- Computer Science
- 2020

Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method.

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