By Topic

Defocused image restoration using RBF network and Kalman filter

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)
Yugang Jiang ; Dept. of Comput. Sci., Beijing Normal Univ., China ; Qing Wu ; Ping Guo

A novel defocused image restoration technique is proposed, which is based on radial basis function (RBF) neural network and Kalman filter. In this technique, firstly a RBF neural network is trained in wavelet domain to estimate defocus parameter. After obtaining the point spread function (PSF) parameter, Kalman filter is adopted to complete the restoration. We experimentally illustrate its performance on simulated data and compare it with other methods. Results show that the proposed PSF parameter estimation technique is more robust to noise.

Published in:

Systems, Man and Cybernetics, 2005 IEEE International Conference on  (Volume:3 )

Date of Conference:

10-12 Oct. 2005