By Topic

A projected gradient algorithm for image restoration under Hessian matrix-norm regularization

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

2 Author(s)
Lefkimmiatis, S. ; Biomed. Imaging Group, EPFL, Lausanne, Switzerland ; Unser, M.

We have recently introduced a class of non-quadratic Hessian-based regularizers as a higher-order extension of the total variation (TV) functional. These regularizers retain some of the most favorable properties of TV while they can effectively deal with the staircase effect that is commonly met in TV-based reconstructions. In this work we propose a novel gradient-based algorithm for the efficient minimization of these functionals under convex constraints. Furthermore, we validate the overall proposed regularization framework for the problem of image deblurring under additive Gaussian noise.

Published in:

Image Processing (ICIP), 2012 19th IEEE International Conference on

Date of Conference:

Sept. 30 2012-Oct. 3 2012