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

Abstract

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 highto 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.Index Terms-image sequence restoration, state space model, maximum likelihood estimator, Kalman filter.

Improvement of tracking capability in the mean shift tracker

“…by a statistical approach maximum likelihood (ML) estimator given in [5], [6]. Here, the states and observations are defined by x k = s h k and z k = s l k from the high-and low-resolution tracking results.…”