Denoising kernels, multilayer perceptrons and autoencoders for electron microscopy
-
Updated
Aug 30, 2018 - Python
Denoising kernels, multilayer perceptrons and autoencoders for electron microscopy
Library of phase imaging functions, classes, and methods.
Adaptive partial scanning transmission electron electron microscopy with reinforcement learning
In this work an automatic code for collection of the CuS nanoparticles thickness and width is suggested. The code is written in Matlab and uses its advanced image processing tools. Bright field TEM images were produced using Ilse Katz Institute for Nanoscale Science & Technology TEM JEM-1230 (T-12). The images were converted from DigitalMicrogra…
Calculate the total generalized oscillator strength for atomic orbitals
Scripts and Tools for Electron Microscopy Image Analysis.
Modeling of the Nup82 subcomplex of the Nuclear Pore Complex
Deep Learning Semantic Segmentation of Catalytic Materials
Panoptic segmentation algorithms for 2D and 3D electron microscopy images
Reduced Compressed Description for Direct Electron Microscopy Data
This repository is official implementation of "EM-stellar: benchmarking deep learning for electron microscopy image segmentation" on Google Colab.
A MATLAB-based program for processing and analysis of nanoSIMS data
Your own customizable "back of the envelope" calculation!
Using deep learning methods to recognize and measure silica spheres in SEM pictures.
4D-STEM acquisition/visualization software including EELS/EDS SI and tomography
Electron Microscopy Particle Segmentation Dataset
Modeling of the TFIIH complex using chemical cross-links and electron microscopy (EM) density maps
Modeling of the yeast Mediator complex
The code for reconstruction algorithm and post-processing of electron tomographic series of colloidal particles acquired in liquid
Add a description, image, and links to the electron-microscopy topic page so that developers can more easily learn about it.
To associate your repository with the electron-microscopy topic, visit your repo's landing page and select "manage topics."