Cart (Loading....) | Create Account
Close category search window
 

Stochastic wavelet-based image modeling using factor graphs and its application to denoising

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
$31 $13
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)
Xiao, S. ; Illinois Univ., Urbana, IL, USA ; Kozintsev, I. ; Ramchandran, K.

In this work, we introduce an efficient hidden Markov field model for wavelet image coefficients and apply it to the image denoising problem. Specifically, we propose to model wavelet image coefficients within subbands as Gaussian random variables with parameters determined by underlying hidden Markov-type process. Our model is inspired by the recent estimation-quantization image coder. To reduce the computational complexity we apply the novel factor graph framework to combine two 1-D chain models to approximate a hidden Markov field (HMF) model. We then apply the proposed models for wavelet image coefficients to perform an approximate minimum mean square error (MMSE) estimation procedure to restore an image corrupted by additive white Gaussian noise. Our results are among the state-of-the-art in the field and they indicate the promise of the proposed modeling techniques

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:6 )

Date of Conference:

2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.