By Topic

Recursive displacement estimation and restoration of noisy-blurred image sequences

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)
Brailean, J.C. ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; Katsaggelos, A.K.

A recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy-blurred image sequence is developed. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of estimates that they are dependent upon. Nonstationary models are used for the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both. The advantage of the proposed SDIE (simultaneous displacement and intensity field estimation) algorithm is that the error inherent in estimating the DVF is taken into account in the filtering of the intensity field. A second advantage is that, through the use of the nonstationary VCGM (vector coupled Gauss-Markov) and STCGM (spatiotemporal coupled Gauss-Markov) models, boundaries in both the DVF and the intensity fields are preserved. The performance of the proposed SDIE algorithm was shown to be superior to that of the Wiener-based PR algorithm and the 2-D Kalman filter in estimating the DVF and intensity field, respectively, from a noisy-blurred image sequence.<>

Published in:

Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on  (Volume:5 )

Date of Conference:

27-30 April 1993