https://github.com/csjunxu/TWSC-ECCV2018

Matlab Code for "A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising, ECCV 2018".
https://github.com/csjunxu/TWSC-ECCV2018

denoising-images image-processing

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Matlab Code for "A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising, ECCV 2018".

• Host: GitHub
• URL: https://github.com/csjunxu/TWSC-ECCV2018
• Owner: csjunxu
• License: other
• Created: 2018-07-04T08:09:10.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-03-24T09:09:54.000Z (over 4 years ago)
• Last Synced: 2024-08-02T11:18:44.615Z (4 months ago)
• Topics: denoising-images, image-processing
• Language: MATLAB
• Homepage:
• Size: 10.2 MB
• Stars: 91
• Watchers: 10
• Forks: 26
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.txt
  • License: License.txt

Awesome Lists containing this project

• awesome-image-denoising-state-of-the-art - [Web - ECCV2018) [[PDF]](http://openaccess.thecvf.com/content_ECCV_2018/papers/XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper.pdf) (Denoising Algorithms)

README

        
The code in this package implements the Trilateral Weighted Sparse Coding Scheme for real color image denoising as described in the following paper:

```

@article{TWSC_ECCV2018,        

         author = {Jun Xu and Lei Zhang and David Zhang},        

         title = {A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising},        

         journal = {ECCV},       

         year = {2018}     

}

```

Please cite the paper if you feel this code useful in your research.

Please see the file License.txt for the license governing this code.

  Version:       1.0 (13/07/2018), see ChangeLog.txt

  Contact:       Jun Xu 

Test

------------

1. Run "Demo_TWSC_Sigma_AWGN.m" for Additive White Gaussian noise removal.

2. Run "Demo_TWSC_Sigma_RW*.m" for Real-world noise removal.

Note: Please set "Original_image_dir" according to your case.

Data

------------

Please download the data from corresponding addresses.

1. cleanimages: 20 high quality commonly used natural gray scale images

2. nc: real noisy images with no ''ground truth''

                        This dataset can be found at http://demo.ipol.im/demo/125/

3. cc: 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.

4. dnd: The Darmstadt Noise Dataset [2] consists of 50 pairs of real noisy images, 

             each images provides 50 crops, resulting overall 1,000 crops provided on

             https://noise.visinf.tu-darmstadt.de/

[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.

[2] Benchmarking Denoising Algorithms with Real Photographs. Tobias Plötz and Stefan Roth. CVPR 2017.

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 [email protected] or [email protected].