Gradient Step Denoiser for convergent Plug-and-Play
Introductory commands
The code was computed with Python 3.8.10, PyTorch Lightning 1.2.6, PyTorch 1.7.1
pip install -r requirements.txt
Gradient Step Denoiser (GS-DRUNet)
The code relative to the Gradient Step Denoiser can be found in the ‘GS_denoising’ directory.
For simply testing the algorithm, the GS-DRUNet pretrained weights can be found in ‘GS_denoising/ckpts/GSDRUNet.ckpt’
Training
- Download training dataset from https://drive.google.com/file/d/1WVTgEBZgYyHNa2iVLUYwcrGWZ4LcN4–/view?usp=sharing and unzip ‘DRUNET’ in the ‘datasets’ folder
- Realize training
cd GS_denoising
python main_train.py --name experiment_name --log_folder logs
Checkpoints, tensorboard events and hyperparameters will be saved in the ‘GS_denoising/logs/experiment_name’ subfolder.
Testing
- Download pretrained checkpoint from https://drive.google.com/file/d/1aafXsJG50FHdNIBYfQZ2jRKTfY0ig6Zi/view?usp=sharing and save it as GS_denoising/ckpts/GSDRUNET.ckpt
- For denoising the whole CBSD68 dataset at input Gaussian noise level 25 :
cd PnP_restoration
python denoise.py --dataset_name CBSD68 --noise_level_img 25
Add the argument --extract_images the save the output images (slower)
Gradient Step PnP (GS-PnP)
Deblurring
- If not already done, download pretrained checkpoint from https://drive.google.com/file/d/1aafXsJG50FHdNIBYfQZ2jRKTfY0ig6Zi/view?usp=sharing and save it as GS_denoising/ckpts/GSDRUNET.ckpt
- For deblurring the CBSD10 images at input Gaussian noise level 7.65, sequentially blurred with the 10 different kernels exposed in the paper:
cd PnP_restoration
python deblur.py --dataset_name CBSD10 --noise_level_img 7.65
Add the argument --extract_images the save the output images (slower)
Super-resolution
For performing super-resolution of CBSD10 images, downscaled with scale sf, Gaussian noise level 7.65, and sequentially blurred with the 8 different kernels exposed in the paper:
cd PnP_restoration
python SR.py --dataset_name CBSD10 --noise_level_img 7.65 --sf 2
Inpainting
cd PnP_restoration
python inpaint.py --dataset_name set3c
