MLI-lab / cs_deep_decoder

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation

This repository provides code for reproducing the figures in the paper:

``Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation'' by Reinhard Heckel and Mahdi Soltanolkotabi. Contact: reinhard.heckel@gmail.com

Organization

  • Figure 1: compressive_sensing_example_convergence.ipynb
  • Figure 5: MRI_multicoil_deep_decoder_accelerate.ipynb

Installation

The code is written in python and relies on pytorch. The following libraries are required:

  • python 3
  • pytorch
  • numpy
  • skimage
  • matplotlib
  • scikit-image
  • jupyter

The libraries can be installed via:

conda install jupyter

The code to reproduce the MRI experiment uses a few function from the fastMRI repository to load the k-space data, those can be obtained by copying the data and common folders from the repository https://github.com/facebookresearch/fastMRI. In particular, download the code from the fastMRI repository, and copy the folder fastMRI/data into the cs_deep_decoder repository.

Citation

@inproceedings{heckel_compressive_2020,
    author    = {Reinhard Heckel and Mahdi Soltanolkotabi},
    title     = {Compressive sensing with un-trained neural networks: {Gradient} descent finds the smoothest approximation},
    booktitle = { {International} {Conference} on {Machine} {Learning} },
    year      = {2020},
}

Licence

All files are provided under the terms of the Apache License, Version 2.0

About


Languages

Language:Jupyter Notebook 98.8%Language:Python 1.2%