Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers).

The data is organized as follows:

- ct_data_sets.tar.gz:  contains preprocessed validation (aka calibration) and test sets with:

• ground truth images,
• FBP reconstructions,
• measurements, for the various settings explored (3 noise levels).

- mri_data_sets.tar.gz: contains preprocessed validation (aka calibration) and test sets with:

• subsampling cartesian masks,
• sensitivity masks,
• ground truth image,
• measurements, for the various settings explored: single- and multi-coil MRI, various acceleration rates (2, 4, and 8), synthetic noise, and different image types (fat suppression or not).

For completeness, the code used to generate the preprocessed data from the raw data can be found on the GitHub repository.