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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
Last synced: 2 months ago
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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 (almost 5 years ago)
- Last Synced: 2024-08-02T11:18:44.615Z (5 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].