We implemented stationary wavelet transform with the help of pytorch wavelets. You need to first install pytorch_wavelets to run wavelet transform.
git clone https://github.com/fbcotter/pytorch_wavelets
cd pytorch_wavelets
pip install .
You need save Mayo Clinic dataset properly. By default, we located our project repository and mayo clinic dataset as follows:
data
├── denoising
│ ├── train
│ │ └── mayo
│ │ ├── full_1mm
│ │ ├── full_3mm
│ │ ├── quarter_1mm
│ │ └── quarter_3mm
│ └── test
│ └── mayo
│ ├── full_1mm
│ ├── full_3mm
│ ├── quarter_1mm
│ └── quarter_3mm
works ── wavelet-ldct-denoising
- Training the model
python train.py --model <model> --datasets <list of data>
- Test the mode
python test.py --model <model> --test_datasets <list of data>
waveletdl is our proposed model trained with