DISGAN: Wavelet-informed discriminator guides GAN to MRI Super-resolution with noise cleaning

@ICCV2023

• Environment
• Super-resolution
• Denoising
• Train
• Cite

---

Environment

Essentially required packaged:

• torch-dwt:
  1. cd torch-dwt
  2. python -m pip install -e .
• multiprocessing
• torch2
• pytorch-lightning

Super-resolution

GT

Denoising

Top:DISGAN(ours) for denoising; bottom:GT with noise

Train from scratch

1. Before training, change the directory path in the configuration files: ./mains/config/*.yml
2. Patch the 3D whole brain volumes to HR patches, by executing the script of ./mains/utils/crop_nifti.py
3. The LR patches will be simulated online while executing the trainig scirpt ./mains/ln_DDP_train.py

python ln_DDP_train.py --model_name 'DWT_D'

Citation

@InProceedings{Wang_2023_ICCV,
    author    = {Wang, Qi and Mahler, Lucas and Steiglechner, Julius and Birk, Florian and Scheffler, Klaus and Lohmann, Gabriele},
    title     = {DISGAN: Wavelet-Informed Discriminator Guides GAN to MRI Super-Resolution with Noise Cleaning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2023},
    pages     = {2452-2461}
}