Title :
Image sequence restoration on state space model identification
Author :
Lu, Xin ; Nishiyama, Kiyoshi
Author_Institution :
Dept. of Comput. & Inf. Sci., Iwate Univ., Morioka
Abstract :
In this paper, the proposed method includes a conceptual frame of state space model (SSM) in order to achieve a general model for accurately estimating the high-resolution image sequence from its incomplete low-resolution observation sequence. Here the parameters of SSM are calculated by a statistic approach - maximum likelihood (ML) estimator. By using the most effective filter of SSM - Kalman filter to estimate, we find that the estimated image sequence is closer to the actual one than the bi-linear interpolation.
Keywords :
Kalman filters; image restoration; image sequences; interpolation; maximum likelihood estimation; SSM-Kalman filter; bilinear interpolation; image sequence restoration; maximum likelihood estimator; state space model identification; Electronic mail; Filters; Image reconstruction; Image restoration; Image sequences; Interpolation; Maximum likelihood estimation; State estimation; State-space methods; Statistics; Kalman filter; image sequence restoration; maximum likelihood estimator; state space model;
Conference_Titel :
SICE Annual Conference, 2008
Conference_Location :
Tokyo
Print_ISBN :
978-4-907764-30-2
Electronic_ISBN :
978-4-907764-29-6
DOI :
10.1109/SICE.2008.4654723
