By Topic

Image restoration with kernel component estimation in singular observation process

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)
Tanaka, A. ; Div. of Syst. & Inf. Eng., Hokkaido Univ., Sapporo, Japan ; Imai, H. ; Miyakoshi, M.

A new approach to restore images degraded by singular observation processes is proposed. Existing image restoration filters usually assume non-singularity of observation processes. Therefore, we can not obtain desirable result by these filters, especially in case that the degradation processes have high singularity. By the way, it is well known that differential images can be assumed to be Laplacian distributed random vectors. In this paper, we propose a new restoration method for singular observation processes based on this statistical knowledge about images. A numerical example is also presented to verify the efficacy of the proposed method.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003