在本篇論文中，在數位視訊與類比視訊同時存在時，我們應用卡門濾波器以還原得到較佳的視訊。數位視訊主要以MPEG編碼，但影像經過DCT轉換再經過量化後會產生失真及變形，可能會有較差的品質出現。類比視訊是加成的高斯白雜訊。所以我們可以應用卡門濾波器於這兩種信號以還原出一張品質比數位視訊還要好的影像來。
影像結構是定義於像點和左、上鄰居點亮度函數的線性關係。也就是用像點間亮度函數的線性關係方程式，可以來決定其影像結構的特性。一般影像分割都以純量做為性質，我們則以線性方程組為性質函數，隱含抽象的概念，無法直接度量。但卻可藉由量測區域和像點合併的誤差，來判斷影像結構的一致性，並由循序最小平方誤差法，推得計算誤差的遞迴公式。
卡門濾波器在影像處理上，很常被用來對受污染的影像做最佳化的估測。由於我們分割影像的方式是以區域性質為依據，所以當完成分割後，在不同的性質區域都會有針對該區域可應用於卡門濾波器的系統參數(區域參數)。
我們應用分割的方法在MPEG還原出來的圖上，取出其區域參數，再用卡門還原的方法，將雜訊圖-類比視訊還原出圖來。由實驗結果，在品質比較不好的MPEG下，我們確實能改善MPEG的品質，但在MPEG品質較佳或是雜訊圖較差，可能就達不到我們的要求。

In this paper, we propose a Kalman filtering method to restore signal when both the digital and analog signal are available. The digital video signal is coded by method of MPEG. The error can be introduced in the quantization process of the block DCT transformation. So the quality of the image from the digital video signal needs to be improved. Considering the analog video signal is corrupted by the Gauss White Noise. We can apply the Kalman filter to these two signals at the same time to restore the image for a better quality.
The image structure is defined to be the linear relationship between pixels with their upper and left neighbors. So we can determinate the image structure property by the linear equations of the pixel gray level. Generally, the image segmentation takes the gray values as the property. In our case we take the linear equations as our property function. This property implies an abstract concept and can’t measure directly. We determine the unity of the image structure by measuring the error from merging the pixel into one region. We achieve a recursive formula for computing the error by the sequential least square error method.
In the signal processing, Kalman filter is used for optimal estimation of the signal corrupted by additive noise. We segment the image by its local property. By our segmentation technique every region has its specific image structure. The structures are system parameters of Kalman filter.
We first utilize the method of segmentation on the image recovered from the MPEG signal to find the local parameters. The results of experiments show that we can improve the images quality when the MPEG signal is not very good.

第一章引言
第二章MPEG動態影像壓縮原理
第三章灰階影像分割
第四章卡門濾波器的回顧
第五章系統實作
第六章實驗結果與分析
第七章結論

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