A cleaned up version of the code for papers:
- De los Reyes, Juan Carlos, and David Villacís. "Optimality conditions for bilevel imaging learning problems with total variation regularization." SIAM Journal on Imaging Sciences 15.4 (2022): 1646-1689.
- De los Reyes, Juan Carlos, and David Villacís. "Interpretable Model Learning in Variational Imaging: A Bilevel Optimization Approach"
The python environment must have numpy, pylops and pyproximal installed.
After cloning the project, cd in the root folder and install the module using pip in experimental mode
$ cd nsbplib
$ pip install -e .$ cd experiments
$ python experiments/learn_optimal_scalar_data_parameter.py $dataset_name $output_folder --size_training_set $size_dataset$ cd experiments
$ python experiments/learn_optimal_patch_data_parameter.py $dataset_name $output_folder --patch_size $patch_size --size_training_set $size_dataset$ cd experiments
$ python experiments/learn_optimal_scalar_reg_parameter.py $dataset_name $output_folder --size_training_set $size_dataset$ cd experiments
$ python experiments/learn_optimal_patch_reg_parameter.py $dataset_name $output_folder --patch_size $patch_size --size_training_set $size_dataset$ cd experiments
$ python experiments/learn_optimal_scalar_data_parameter_deblurring.py $dataset_name $output_folder --size_training_set $size_dataset$ cd experiments
$ python experiments/learn_optimal_patch_data_parameter_deblurring.py $dataset_name $output_folder --patch_size $patch_size --size_training_set $size_datasetFor regenerating the plots presented in the paper, there are several scripts that generate the plots and tables
$ python plotting/plot_scalar_reconstruction $output_folder/$dataset_name$ python plotting/plot_reconstructions $output_folder_1 $output_folder_2 ...This script generates the plot regarding the validation error of the learned parameter for different patch sizes and different training set sizes.
$ python plotting/plot_validation $validation_dataset_path $output_folder_1 $output_folder_2 ...This script generates the performance tables for the learned parameters for different patch sizes and different training set sizes.
$ python tables/generate_performance_tables.py $output_folder_1 $output_folder_2 ...
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