By Topic

Constrained Kalman filtering for image restoration

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 $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)
A. G. Qureshi ; Microtel Pacific Res., Burnaby, BC, Canada

The author considers the incorporation of deterministic a priori signal information in the Kalman filtering of images. This information, in the form of constraints, is used to achieve `ringing' reduction by adaptive regularization of the restoration filter. The signal constraints are first transformed into constraints on the Kalman gain. Constrained optimization of the Kalman gain is then implemented using a penalty function approach. The constraints considered are bounds on signal amplitude and signal local variance. The proposed scheme provides a hybrid image restoration that minimizes the mean square error subject to the given a priori constraints. A simple but effective heuristic Kalman algorithm, which uses a `tuning' parameter to achieve ringing reduction, is proposed

Published in:

Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on

Date of Conference:

23-26 May 1989