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State-space model identification and Kalman filtering for image sequence restoration

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2 Author(s)
Xin Lu ; Dept. of Comput. & Inf. Sci., Iwate Univ., Morioka ; Nishiyama, K.

A novel image restoration method is proposed to resolve a problem that the traditional restoration method performs poorly when the kind of image degradation model from high- to low-resolution is unconfirmed. 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, so that the proposed method can be used to improve the restoration results.

Published in:

Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on

Date of Conference:

12-15 Oct. 2008