https://github.com/csjunxu/PGPD_Offline_BID

For GRF 17-18
https://github.com/csjunxu/PGPD_Offline_BID

Last synced: 3 months ago
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For GRF 17-18

• Host: GitHub
• URL: https://github.com/csjunxu/PGPD_Offline_BID
• Owner: csjunxu
• License: other
• Created: 2016-10-04T05:30:55.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-10-04T05:32:39.000Z (over 8 years ago)
• Last Synced: 2024-08-02T11:18:57.039Z (6 months ago)
• Language: Matlab
• Size: 5.93 MB
• Stars: 6
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.txt
  • Changelog: ChangeLog.txt
  • License: License.txt

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• awesome-image-denoising-state-of-the-art - [Web

README

        
% ===============================================================

The code in this package implements the Patch Group Prior Denoising (PGPD) method for

image denoising as described in the following paper:

  Jun Xu, Lei Zhang, Wangmeng Zuo, David Zhang, and Xiangchu Feng,

  Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising.

  IEEE Int. Conf. Computer Vision (ICCV), Santiago, Chile, December 2015.

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 (04/09/2015), see ChangeLog.txt

  Contact:       Jun Xu 

% ===============================================================

Overview

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

The code for learning Patch Group Prior is implemented in the folder "PG-GMM_Traning", which relies

on the training images in the subfolder "Kodak24".

The function "Demo_denoising" demonstrates denoising with the learned Patch Group Prior models 

introduced in the paper, which can all be found in the folder "model".

Dependency

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

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

Contact

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

If you have questions, problems with the code, or find a bug, please let us know. Contact Jun Xu at 

[email protected] or the email provided on my website at www.wangliuqing.tk.