lzhengchun/dn-tutorial

tutorial on noise and/or artifacts removal using convolution neural network

The tutorial is currently composed of three notebooks:

cnn4denoise.ipynb how to build and train a basic convolution neural networks to denoise a synthesized dataset
mdl-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.

Using the Notebooks

The notebooks are designed to be easy to use on the cloud or on your own systems.

Try in on Binder

without installing anything locally.

Running 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

Running on Google Colab

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 past https://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)

Citation

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}
}

lzhengchun / dn-tutorial