New Check the extension of CRR-NNs to weakly-convex regularizers preprint/repo. Remark: the latter repository also includes a multi-noise-level deep-equilibrium training of CRR-NNs with a slightly simpler procedure (shared activations + no spline regularization), with same denoising performance.
Implementation of the CRR-NNs as presented in this paper (or open access version). For any bug report/question/help needed please contact us.
Nb: detailed information is provided in the README.me of each folder.
The repository is organized as follows:
- trained_models: contains a few instances of trained CRR-NNs.
- tutorial: to understand and deploy trained CRR-NNs for solving inverse problems.
- inverse_problems: contains the MRI and CT experiments + generic utils for solving inverse problems and for the automated validation and testing.
- denoising: to reproduce the denoising results on BSD68 with the provided trained models.
- training: to reproduce training.
- under_the_hood: to visualize the NNs (filters and activations).
- models: contains the spline modules, the CRR-NNs class, some optimization schemes (t-step denoiser and accelerated gradient descent)...
-
hyperparameter_tuning: helpers and discussion on the tuning of
$\lambda$ and$\mu$ for solving inverse problems. See inverse_problems folder for usage.
- python >= 3.8
- pytorch >= 1.12
- (optional) CUDA
- numpy
- pandas
- tensorboard
- matplotlib
- Pillow
For the CT experiments:
- astra-toolbox
For the MRI expeirements:
- fastmri
- bart