By Topic

Non-quadratic Regularization Based Image Deblurring: Automatic Parameter Selection and Feature Based Evaluation

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)
Batu, O. ; Muhendislik ve Doga Bilimleri Fak., Sabanci Univ., Istanbul ; Cetin, M.

In computer vision based analysis, a completely automatic inspection of parts on assembly line involves many challenges. Since the parts are moving fast on line it is most probable that the captured frames are motion blurred and noisy images. Therefore accurate extraction of features from the image may not be possible. To overcome this challenge, we consider quadratic and non-quadratic regularization based deblurring. To select the regularization parameter automatically, we propose usage of unbiased predictive risk estimator method. We investigate the quantitative effect of the applied methods on feature extraction performance and demonstrate the effectiveness of the proposed approach with experiments on real data.

Published in:

Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th

Date of Conference:

11-13 June 2007