By Topic

Maximum likelihood parametric blur identification based on a continuous spatial domain model

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

2 Author(s)
G. Pavlovic ; Dept. of Electr. Eng., Rochester Univ., NY, USA ; A. M. Tekalp

A new formulation is proposed for maximum likelihood (ML) blur identification that is based on a parametric description of the blur in the continuous spatial coordinates. The aim of this formulation is to find the ML estimate of the extent of certain point spread functions (PSF). It is shown that this can be achieved by formulating the problem in the continuous spatial coordinates, as opposed to using the conventional discrete spatial domain model. Experimental results are presented for the cases of uniform motion blur, out of focus blur and truncated Gaussian blur at various signal-to-noise ratios

Published in:

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Date of Conference:

14-17 Apr 1991