本文發展一種數位影像在粗細像素之間重建數位影像的理想化模式。將一設定物件做短距離位移後，構建成兩張理想化的數位影像，再從第一張圖之點，計算在第二張圖上各點之局部互相關系數，最後取系數極大時之位置，當作在第二張圖上對應於第一張圖該點之位置。定位誤差是由理論位置和估算位置之差異的距離決定。本文發現下列三種方法可以明顯的降低定位誤差：將灰階值加上最大灰階值之均勻性平移，使用單調性分段三次曲線內插法建立細像素後，進行次像素估算定位，和使用修正型之選擇性Shepard內插法。本文亦討論線性守恆性內插法和LoG濾波器之邊緣線估算法，發現前者可以略為改進定位誤差有時也有助於影像的可視性，而後者則在細像素估算有潛力。本文亦應用到數個車牌影像的實例上，以討論本文的模式實用之可能性。

　　An idealized model on re-constructing digital image between coarse and fine pixel image is proposed. The maximum local cross-correction coefficient method is employed to identify the associated point of an digital image with respect to another digital image. The distance between two corresponding points is considered as the displacement between points. As comparing with the exact distance, the identification error can be defined. It is found that the following methods can improve the error on identification: shift the gray level value by an amount approximately equal to the maximum gray level of two images; construct the fine pixel image via the monotonic cubic spline interpolation to perform the fine grid identification; and employs a modified Shepard interpolation to perform selected interpolation which excludes all points with an extra- ordinary displacement. The linear conservative interpolation method is examined and is found to have a small effect of improving identification error because the proposed method of calculating the gray level gradient is improper. Sometimes, it is helpful to improve image visibility. The LoG filter is found to have potential to improve the estimation of edge image line in the fine grid calculation. Several motor plate images were employed to test the applicability of the proposed model.

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