By Topic

Grey theory applied in non-subsampled Contourlet transform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Li, H.-J. ; Coll. of Automat. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China ; Zhao, Z.-M. ; Yu, X.-L.

This study mainly discussed the application of grey theory in the non-subsampled Contourlet domain. The new algorithm combined the excellent characteristics of the non-subsampled Contourlet transform and grey theory in image denoising. The total variation model is used first to modify the noised image in order to reduce the pseudo-Gibbs artifacts. In the high-frequency area, the grey relational methods are proposed in the current study. The authors combined the two methods in the high-frequency processing and proposed an improved model that is superior to others. Finally, they presented some experimental results to compare with the non-local means algorithm. The comparisons showed very good performance of the proposed model. The proposed method can preserve most important information of image.

Published in:

Image Processing, IET  (Volume:6 ,  Issue: 3 )