In this paper, we propose an edge-adaptive image modeling approach to preserve edges with greater noise reduction. The general Kalman filter for image restoration removes most of the high-frequency components from the image thus reducing the noise. However, this also has the undesirable effect of smoothing edge resulting in blurring or ringing. The proposed method classifies the pixels of an observed image in the flat region and the four edge regions, and estimates the image model for each region. It is possible to restore an image more accurately without excessive smoothing. The five Kalman filters which are designed for each part perform the restoration adaptively in each region. As a result, we obtain an accurate image while preserving the edges.
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