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Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising, ICCV 2017.
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Real_ccnoise_denoised_part
.gitattributes 🎉 Added .gitattributes & .gitignore files Jul 26, 2017
.gitignore 🎉 Added .gitattributes & .gitignore files Jul 26, 2017
Block_Matching.m add code Jul 26, 2017
Block_Matching_Real.m add code Jul 26, 2017
ChangeLog.txt add Jul 26, 2017
ClosedWNNM.m add code Jul 26, 2017
Demo_MCWNNM_ADMM1.m add Jul 13, 2018
Demo_MCWNNM_ADMM1_NL_RealGT.m add Oct 28, 2018
Demo_MCWNNM_ADMM1_NL_RealNoGT.m add Jul 13, 2018
Demo_MCWNNM_ADMM2.m add Jul 13, 2018
Image2Patch.m add code Jul 26, 2017
License.txt add code Jul 26, 2017
MCWNNM_ADMM.m add code Jul 26, 2017
MCWNNM_ADMM1.m add code Jul 26, 2017
MCWNNM_ADMM1_Denoising.m add code Jul 26, 2017
MCWNNM_ADMM1_Estimation.m add code Jul 26, 2017
MCWNNM_ADMM1_NL_Denoising.m add code Jul 26, 2017
MCWNNM_ADMM1_NL_Estimation.m add Dec 10, 2018
MCWNNM_ADMM2.m add code Jul 26, 2017
MCWNNM_ADMM2_Denoising.m add code Jul 26, 2017
MCWNNM_ADMM2_Estimation.m add code Jul 26, 2017
MCWNNM_ADMM_Denoising.m add code Jul 26, 2017
MCWNNM_ADMM_Estimation.m add code Jul 26, 2017
MCWNNM_ADMM_NL1.m add code Jul 26, 2017
NoiseEstimation.m add code Jul 26, 2017
PGs2Image.m add Aug 15, 2017
PossibleExtension.txt add Jul 26, 2017
Readme.txt add Aug 15, 2017
SearchNeighborIndex.m add code Jul 26, 2017
cal_ssim.m add code Jul 26, 2017
csnr.m add code Jul 26, 2017
image2cols.m add code Jul 26, 2017

Readme.txt

% ===============================================================
The code in this package implements the Multi-channel Weighted Nuclear Norm Minimization
(MCWNNM) model for real color image denoising as described in the following paper:

 @article{MCWNNM,
 	author = {Jun Xu and Lei Zhang and David Zhang and Xiangchu Feng},
 	title = {Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising},
 	journal = {ICCV},
 	year = {2017}
 }

Please cite the paper if you are using this code in your research.
Please see the file License.txt for the license governing this code.

  Version:       1.0 (26/07/2017), see ChangeLog.txt
  Contact:       Jun Xu <csjunxu@comp.polyu.edu.hk, nankaimathxujun@gmail.com>
% ===============================================================

Notice:
------------
1. We are still optimize the code. In fact, this model can achieve much better performance followed by the
suggestions in the file ''PossibleExtension.txt''. 

2. The optimized code is provided in https://github.com/csjunxu/MCWNNM_ICCV2017.

3. If you want to follow this work and has no ideas, please read ''PossibleExtension.txt''.

Overview
------------
The function "Demo_MCWNNM_ADMM[1||2]" demonstrates color image denoising with the MCWNNM
models introduced in the paper. They use the same model, but with different settings. You can choose 
any setting for your purpose.

The function "Demo_MCWNNM_ADMM1_NL_RealGT" demonstrates real color image denoising with 
"ground truth" by the MCWNNM models introduced in the paper.

The function "Demo_MCWNNM_ADMM1_NL_RealNoGT" demonstrates real color image denoising 
without "ground truth" by the MCWNNM models introduced in the paper.


Data
------------
Please download the data from corresponding addresses.
1. kodak_color: 24 high quality color images from Kodak PhotoCD dataset
                        This dataset can be found at http://r0k.us/graphics/kodak/
2. NoiseClinicImages: real noisy images with no ''ground truth''
                        This dataset can be found at http://demo.ipol.im/demo/125/
3. Real_ccnoise_denoised_part: 15 cropped real noisy images from CC [1]. 
                        This dataset can be found at  http://snam.ml/research/ccnoise
                        The smaller 15 cropped images can be found on in the directory 
                        ''Real_ccnoise_denoised_part'' of 
                        https://github.com/csjunxu/MCWNNM_ICCV2017
                                                The *real.png are noisy images;
                                                The *mean.png are "ground truth" images;
                                                The *ours.png are images denoised by CC.

[1] A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising. 
     Seonghyeon Nam*, Youngbae Hwang*, Yasuyuki Matsushita, Seon Joo Kim, CVPR, 2016.

Dependency
------------
This code is implemented purely in Matlab2014b and doesn't depends on any other toolbox.

Contact
------------
If you have any questions or suggestions with the code, or find a bug, please let us know. 
Contact Jun Xu at csjunxu@comp.polyu.edu.hk or nankaimathxujun@gmail.com.


Update:
1. 08/15/2017: Complement the "PGs2Image.m" function. 
Thanks Zi-Fa Han for pointing out the missing of this function. 
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