This code accompanies
- Hendriksen, A. A., Pelt, D. M., & Batenburg, K. J. (2020). Noise2Inverse: self-supervised deep convolutional denoising for linear inverse problems in imaging. CoRR, (), .
Create a conda environment with:
conda env create -f environment.yaml
conda activate noise2inverse
# Install noise2inverse package in environment
pip install -e .Please be sure to use the exact ASTRA Toolbox version, since newer versions can result in different pixel intensities.
There is no windows build of the MSD network available for Windows. We have added an environment file for Windows that does not include the MSD network.
Create a conda environment with:
conda env create -f environment_windows.yaml
conda activate noise2inverse
# Install noise2inverse package in environment
pip install -e .In the training and evaluation notebooks, make sure to select UNet or
DnCNN as the network. The "msd" option is not available. The
repository does not contain a trained network weights for UNet and
DnCNN. To evaluate the results, you must first run training.
The notebooks describe how to use the package:
- 01_generate_projections.ipynb: Generates clean and noisy projections of the foam phantom;
- 02_reconstruct.ipynb: Contains code for reconstruction;
- 03_train.ipynb: Trains the network;
- 04_evaluate.ipynb: Applies the trained network to the noisy reconstructions to obtain a denoised output.
- 05_metrics.ipynb: Describes metric calculation.
Metrics and results may slightly differ from reported results in paper: the network was retrained using this cleaned up code.
Examples of intermediate results are added to the git repository. Committing all data files and intermediate results is not possible due to space constraints on github. Nonetheless, using these files, it should be possible to reproduce the results in the paper.