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

页码：553 / 556

页数：4

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