### Install Latest ODL Version

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Install the latest version of the ODL library via pip. This is required for the project's functionality.

```bash
pip install https://github.com/odlgroup/odl/archive/master.zip --upgrade
```

--------------------------------

### Install BART Toolbox

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Download, unzip, and compile the BART Toolbox within the utils directory for MRI imaging.

```bash
cd utils && wget https://github.com/mrirecon/bart/archive/refs/tags/v0.8.00.zip && unzip v0.8.00.zip && rm v0.8.00.zip && cd bart-0.8.00 && make && cd .. && cd ..
```

--------------------------------

### Create Conda Environment

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Use this command to create a conda environment with the necessary Python packages for the project. Ensure you have conda installed.

```bash
conda env create --file=environment.yaml
```

--------------------------------

### Clone and Move Training Data

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Clone the weakly convex ridge regularizer repository and move the BSD training data to the appropriate directory for preprocessing.

```bash
git clone git@github.com:axgoujon/weakly_convex_ridge_regularizer.git && mv weakly_convex_ridge_regularizer/training/data/images/BSD utils/images utils/images
```

--------------------------------

### Denoise Image

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Run the denoising script with the --rr='crr' flag for the standard mask, or --rr='wcrr' for WCRR. Use -m to disable the mask and -n to change noise level. --vis visualizes the mask.

```bash
python run_denoise.py --rr='crr'
```

--------------------------------

### Project Citation

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

This is the BibTeX entry for the paper 'Stability of Data-Dependent Ridge-Regularization for Inverse Problems'.

```bibtex
@article{NA2024,
    author    = {Neumayer, Sebastian and Altekrüger, Fabian},
    title     = {Stability of Data-Dependent Ridge-Regularization for Inverse Problems},
    journal   = {Inverse Problems},
    volume    = {41},
    number    = {6},
    year      = {2025}
}
```

--------------------------------

### Train Regularization Mask

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Train the regularization mask using the preprocess_BSD.py script. Use --rr='crr' for the standard mask or --rr='wcrr' for the WCRR mask.

```bash
python train_mask.py --rr='crr'
```

--------------------------------

### Reconstruct Superresolution Image

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Reconstruct a high-resolution image using run_SiC.py. Use --rr='crr' for the standard mask. Use -val for validation and -m to disable the mask.

```bash
python run_SiC.py --rr='crr'
```

--------------------------------

### Reconstruct MRI Scan

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Reconstruct an MRI scan using run_MRI.py. Use --rr='crr' for the standard mask, -c='single' for singlecoil, or -c='multi' for multicoil. Use -val for validation and -m to disable the mask.

```bash
python run_MRI.py --rr='crr' -c='single'
```

--------------------------------

### Finetune MRI Mask

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Adjust the regularization mask for MRI data using finetune_mask.py. Use -fmri for MRI, --rr='crr' for the standard mask, or --rr='wcrr' for WCRR.

```bash
python finetune_mask.py -fmri --rr='crr'
```

--------------------------------

### Finetune CT Mask

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Adjust the regularization mask for CT data using finetune_mask.py. Use -fct for CT, --rr='crr' for the standard mask, or rr='wcrr' for WCRR.

```bash
python finetune_mask.py -fct --rr='crr'
```

--------------------------------

### Reconstruct CT Scan

Source: https://github.com/fabianaltekrueger/dataadaptiverr/blob/main/README.md

Reconstruct a CT scan using run_CT.py. Use --rr='crr' for the standard mask. Use -val for validation, -m to disable the mask, and -s='limited' for limited-angle setting.

```bash
python run_CT.py --rr='crr'
```

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