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

Application of the UPRE Method to Optimal Parameter Selection for Large Scale Regularization Problems

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

1 Author(s)
Youzuo Lin ; Dept. of Math. & Stat., Arizona State Univ., Tempe, AZ

Regularization is an important method for solving a wide variety of inverse problems in image processing. In order to optimize the reconstructed image, it is important to choose the optimal regularization parameter. The unbiased predictive risk estimator (UPRE) has been shown to give a very good estimate of this parameter. Applying the traditional UPRE is impractical, however, in the case of inverse problems such as deblurring, due to the large scale of the associated linear problem. We propose an approach to reducing the large scale problem to a small problem, significantly reducing computational requirements while providing a good approximation to the original problem.

Published in:

Image Analysis and Interpretation, 2008. SSIAI 2008. IEEE Southwest Symposium on

Date of Conference:

24-26 March 2008

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.