Source code for neural networks that complete scanning transmission electron micrographs from partial scans. This can be used to decrease electron dose and scan time, to reduce damage to samples. In addition to example code, there are sets of code used to investigate systematic errors and for spiral scans selected with binary masks for a range of coverages. Links to pre-trained models are provided
When pharmaceutical companies develop new drugs or vaccines there are large amounts of data in the f...
Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been s...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for neural networks that supersample scanning transmission electron micrographs. This ca...
Source code for a neural network based on Xception that improves electron micrograph signal-to-noise...
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam e...
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron mic...
Dataset containing the jupyter notebook used to construct the database of image, to model and train ...
We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for electron micrograph restora...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
This work deals with the use of a convolutional neural network in the area of segmentation of images...
Low electron dose observation is indispensable for observing various samples using a transmission el...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
We present an atrous convolutional encoder-decoder trained to denoise 512×512 crops from electron mi...
When pharmaceutical companies develop new drugs or vaccines there are large amounts of data in the f...
Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been s...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...
Source code for neural networks that complete scanning transmission electron micrographs from partia...
Source code for neural networks that supersample scanning transmission electron micrographs. This ca...
Source code for a neural network based on Xception that improves electron micrograph signal-to-noise...
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam e...
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron mic...
Dataset containing the jupyter notebook used to construct the database of image, to model and train ...
We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for electron micrograph restora...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
This work deals with the use of a convolutional neural network in the area of segmentation of images...
Low electron dose observation is indispensable for observing various samples using a transmission el...
Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quan...
We present an atrous convolutional encoder-decoder trained to denoise 512×512 crops from electron mi...
When pharmaceutical companies develop new drugs or vaccines there are large amounts of data in the f...
Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been s...
Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM...