Gradient Step Denoiser for convergent Plug-and-Play

[Paper]

Samuel Hurault, Arthur Leclaire, Nicolas Papadakis.
Institut de Mathématiques de Bordeaux, France.

Prerequisites

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.

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.

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 and --extract_curves the save convergence curves.

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

Inpainting on set3C images, with randomly masked pixels (with probability prop_mask = 0.5) sequentially blurred with the 10 different kernels exposed in the paper:

cd PnP_restoration
python inpaint.py --dataset_name set3c --prop_mask 0.5

Acknowledgments

This repo contains parts of code taken from :

• Deep Plug-and-Play Image Restoration (DPIR) : https://github.com/cszn/DPIR