Image Sequence Restoration Based on State Space Model Identification

被引：0
|
作者：
Lu, Xin [1 ]
Nishiyama, Kiyoshi [1 ]
机构：
[1] Iwate Univ, Dept Comp & Informat Sci, Morioka, Iwate 020, Japan
来源：
2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7 | 2008年
关键词：
image sequence restoration; state space model; maximum likelihood estimator; Kalman filter;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：553 / 556
页数：4
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