DIP-denosing
This is a code repo for Rethinking Deep Image Prior for Denoising (ICCV 2021).
Addressing the relationship between Deep image prior and effective degrees of freedom, DIP-SURE with STE(stochestic temporal ensemble) shows reasonable result on single image denoising.
If you use any of this code, please cite the following publication:
@article{jo2021dipdenoising,
author = {Yeonsik Jo, Se young chun, and Choi, Jonghyun},
title = {Rethinking Deep Image Prior for Denoising},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {5087-5096}
}
Working environment
- TITAN Xp
- ubuntu 18.04.4
- pytorch 1.6
Note: Experimental results were not checked in other environments.
Set-up
- Make your own environment
conda create --name DIP --file requirements.txt
conda avtivate DIP
pip install tqdm
Inference
- Produce CSet9 result
bash exp_denoising.sh CSet9 <GPU ID>
- For your own data with sigma=25 setup
mkdir testset/<YOUR_DATASET>
python main.py --dip_type eSURE_new --net_type s2s --exp_tag <EXP_NAME> --optim RAdam --force_steplr --desc sigma25 denoising --sigma 25 --eval_data <YOUR_DATASET>
Browsing experimental result
- We provide reporting code with invoke.
invoke showtable csv/<exp_type>/<exp_tag>
- Example.
invoke showtable csv/poisson/MNIST/
PURE_dc_scale001_new optimal stopping : 384.30, 31.97/0.02 | ZCSC : 447.60, 31.26/0.02 | STE 31.99/0.02
PURE_dc_scale01_new optimal stopping : 94.70, 24.96/0.12 | ZCSC : 144.60, 24.04/0.14 | STE 24.89/0.12
PURE_dc_scale02_new optimal stopping : 70.30, 22.92/0.20 | ZCSC : 110.00, 21.82/0.22 | STE 22.83/0.20
<EXEPRIMENTAL NAME> optimal stopping :<STEP>, <PSNR>/<LPIPS> | ZCSC : <STEP>, <PSNR>/<LPIPS>| STE <PSNR>/<LPIPS>
The reported numbers are PSNR/LPIPS.
Results in paper
For the result used on paper, please refer this link.
SSIM score
For SSIM score of color images, I used matlab code same as the author of S2S.
This is the demo code I received from the S2S author.
Thank you Mingqin!
% examples
ref = im2double(imread('gt.png'));
noisy = im2double(imread('noisy.png'));
psnr_result = psnr(ref, noisy);
ssim_result = ssim(ref, noisy);
License
MIT license.
Contacts
For questions, please send an email to [email protected]
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