iRestNet

Unfolded proximal interior point algorithm for image deblurring

• License : GNU General Public License v3.0
• Author : Marie-Caroline Corbineau
• Institution : Centre de Vision Numérique, CentraleSupélec, Inria, Université Paris-Saclay
• Email : mariecaroline.corbineau@gmail.com
• Related publication: https://arxiv.org/abs/1812.04276
• Please use the following citation:

  Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet and Marco Prato, `Deep unfolding of a proximal interior point method for image restoration'. To appear in Inverse Problems, doi:10.1088/1361-6420/ab460a (2019).

Installation

1. Install miniconda.
2. Create an environment with python version 3.6.5.

   $ conda create -n iRestNet_env python=3.6.5
   

3. Inside this environment install the following packages.

   $ conda activate iRestNet_env
   $ conda install pytorch=0.4.0 cuda80 -c pytorch
   $ pip install torchvision==0.2.1 matplotlib==3.0.2 numpy==1.16.0 jupyterlab opencv-python==4.0.0.21 scipy==1.2.0 tqdm==4.29.1 scikit-image==0.14.2
   

4. Use the demo notebook to test and train iRestNet models.

Files organization

• Datasets
  • BSD500_COCO1000_train_val
    • train: contains 200 RGB training images from the Berkeley segmentation dataset (BSD500) and 1000 RGB images from the COCO dataset
    • val: contains 100 RGB validation images from the Berkeley segmentation dataset (BSD500)
  • Groundtruth: contains groundtruth test images
    • full
      • BSD500: contains 200 groundtruth test images from the Berkeley segmentation dataset
      • Flickr30: contains 30 groundtruth test images from Flickr30
    • cropped: contains cropped test images (created by the function create_testset)
  • Testsets: contains a folder for each test configuration including associated blur kernels (kernel.mat)
• Model_files : contains iRestNet files
  • Deg3PolySolver.py: contains the cardan class used to compute the proximity operator of the logarithmic barrier
  • iRestNet.py: contains iRestNet_class, which is the main class including train and test functions
  • model.py: includes the definition of the layers in iRestNet
  • modules.py: useful functions used in iRestNet
  • tools.py: contains some useful side functions
• PyTorch_ssim : forward and backward functions to use SSIM as the training loss, we use the code from https://github.com/Po-Hsun-Su/pytorch-ssim
• Trainings : contains the models used in https://arxiv.org/abs/1812.04276 for every test configuration. In the demo notebook, the results of the aforementioned models on the testsets are saved in this folder.

Demo file

demo.ipynb: shows how to test and train iRestNet

Thank you for using our code, kindly report any suggestion to the corresponding author.