The tutorial is currently composed of three notebooks:
cnn4denoise.ipynb
how to build and train a basic convolution neural networks to denoise a synthesized datasetmdl-prod-toy.ipynb
Assmue you train the model on a cluster or desktop that has an NVIDIA GPU and save your model to a file. THis notebook explains how to load the trained model and run it on your laptop to denoise your images.TomoGAN-prod.ipynb
explains how to train the more advanced TomoGAN for real scientific images, and use it in real world.
The notebooks are designed to be easy to use on the cloud or on your own systems.
without installing anything locally.
The environment needed for the notebook is described in environment.yml
First, install Anaconda then use Anaconda's command line tool to build the environment:
conda env create --file environment.yml
- go to
https://colab.research.google.com/
then log in your Google account - Try to go to
File -> Open
if the following box does not show up - Hit the
GitHub
tab shown in the open box, then pasthttps://github.com/AIScienceTutorial/Denoising
as shown in the following screenshot and hit the search button - Choose and hit a notebook that you want to run.
- play it and enjoy(hopefully)
If you find this material useful for your research, please consider cite our paper(s):
-
Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu and Ian Foster, "Deep Learning Accelerated Light Source Experiments," 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), Denver, CO, USA, 2019, pp. 20-28, doi: 10.1109/DLS49591.2019.00008.
-
Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Doga Gursoy, Francesco De Carlo, and Ian Foster, "TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion," J. Opt. Soc. Am. A 37, 422-434 (2020)
Or via bibtex
@inproceedings{liu2019deep,
title={Deep Learning Accelerated Light Source Experiments},
author={Zhengchun Liu and Tekin Bicer and Rajkumar Kettimuthu and Ian Foster},
year={2019},
booktitle={2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)},
pages={20--28},
doi={10.1109/DLS49591.2019.00008}
}
@article{liu2020tomogan,
title={TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion},
author={Liu, Zhengchun and Bicer, Tekin and Kettimuthu, Rajkumar and Gursoy, Doga and De Carlo, Francesco and Foster, Ian},
journal={Journal of the Optical Society of America A},
volume={37},
number={3},
pages={422--434},
year={2020},
doi={10.1364/JOSAA.375595},
publisher={Optical Society of America}
}