Extrapolated Plug-and-Play Three-Operator Splitting Methods for Nonconvex Optimization with Applications to Image Restoration
This is the code of Wu Z, Huang C, Zeng T. Extrapolated Plug-and-Play Three-Operator Splitting Methods for Nonconvex Optimization with Applications to Image Restoration[J]. arXiv preprint arXiv:2403.01144, 2024. Accepted by SIAM journal on Image Science, 2024.
[paper] https://arxiv.org/pdf/2403.01144.pdf
[Prerequisites] following the training in https://github.com/samuro95/Prox-PnP
You can download our pretrained checkpoint at https://drive.google.com/file/d/1fmCBl8lV8KGezIaH4SioqwJcySDUafD1/view?usp=sharing
Please save the trained model in the ckpts directory: /GS_denoising/ckpts/Prox-DRUNet.ckpt
You can test after downloading the .ckpt file as follows.
cd PnP_restoration
For deblurring with different noise levels (2.55, 7.65, 12.75) under blur kernel 1 in the paper
python3 deblur.py --dataset_name set3c --PnP_algo DYSdiff --noise_level_img 2.55 --noise_level_img_ini 2.55 --alpha 0.5 --alp0 0.2
python3 deblur.py --dataset_name set3c --PnP_algo DYSdiff --noise_level_img 7.65 --noise_level_img_ini 7.65 --alpha 0.5 --alp0 0.2
python3 deblur.py --dataset_name set3c --PnP_algo DYSdiff --noise_level_img 12.75 --noise_level_img_ini 12.75 --alpha 0.5 --alp0 0.2
to see our DeTik results in Table 1 of the paper.
For super-resolution with sf 2 and 3, different noise levels (2.55, 7.65, 12.75) under blur kernel 1 in the paper
python3 SR.py --dataset_name Set5 --PnP_algo DYSdiff --noise_level_img 2.55 --noise_level_img_ini 2.55 --alp0 0.2 --sf 2
python3 SR.py --dataset_name Set5 --PnP_algo DYSdiff --noise_level_img 7.65 --noise_level_img_ini 7.65 --alp0 0.2 --sf 2
python3 SR.py --dataset_name Set5 --PnP_algo DYSdiff --noise_level_img 12.75 --noise_level_img_ini 12.75 --alp0 0.2 --sf 2
to see our DeTik results in Table 2 of the paper.
for more blur kernel results, please check lines 23 and 85 in deblur.py
dataset_name is your test data
PnP_algo is your designed algorithm
noise_level_img is the noise you add
noise_level_img_ini is for the initial noise input
alp0 is the extrapolated parameter of the algorithm
by chaoyan 20 March, 2024