GAN-STEM-Conv2MultiSlice - Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

Last update: Feb 10, 2021

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Overview

GAN-STEM-Conv2MultiSlice

GAN method to help covert lower resolution STEM images generated by convolution methods to higher resolution STEM images generated by Multi-Slice methods. We published the results in this Arxiv paper, you can check more details from there, Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

Files Usage

`Calculate_NRMSE.py`

Calculate_NRMSE.py helps calculate the normalized root mean square error of two images generated by different methods.

To run the command you need first download the data file from Google Drive and name the folder Data. Then you can run the program with this command.

python Calculate_NRMSE.py

You need Python 3 and scikit-image,numpy and matplotlib to run the program .

To change to a different fold of images just comment currently used folder path then uncomment the previously commented folder path.

GAN

Based on Keras-GAN we have obtained the initial results which shows very promising results.

GAN-STEM-Conv2MultiSlice - Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

Related tags

Overview

GAN-STEM-Conv2MultiSlice

Files Usage

`Calculate_NRMSE.py`

GAN

Owner

UW-Madison Computational Materials Group

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