在影像感測器 CMOS Image Sensor的效能考量上，動態範圍是一個重要的指標。高動態範圍的影像感測器具有同時對高、低亮度感應的良好能力，所以高動態範圍一直都是影像感測器設計的重心。近年來，藉由對景物進行多重取樣而達到高動態範圍的方法被提出，對於採用多重取樣方法的影像感測器設計而言，需要更有效的改良方法來增強感測器的效能。

　　在本論文中，我們提出一個改良式可預測性的取樣方法，使得影像感測器達到線性高動態範圍。我們基本的理論是藉由光強度臨界值來判斷像素信號是否達到飽和，然後選擇理想的積分時間。我們提出一個方法來避免影像感測器因為多重取樣方法所產生持續的重置與積分動作，進而造成系統耗費相當多的存取時間與功率消耗。我們提出的方法可以在較短的積分時間內根據入射光強度的大小選擇相對的理想積分時間，並且利用較短的積分時間(即理想的積分時間)，避免原本因為高入射光強度所產生的像素飽和情況，然後基於時間比例關係，等比例放大因為較短積分時間所累積的像素信號值，因而使得整個影像感測器更加有效率。

　Sensor dynamic range is an important figure of merit for image sensors. Dynamic range quantifies the ability to adequately image both high lights and dark shadows in a scene, so wide dynamic range is the key factor for the design of CMOS image sensors. Recently, specialized linear integrating image sensors have achieved wide dynamic range by using multiple samples of a natural scene.

　In this thesis, we proposed a new approach that relies on the improved predictive sampling technique to achieve wide dynamic range. Our Basic theorem is to compare the light intensity with the intensity threshold, and then we can predict the pixel saturation. The optimal integration time for a pixel that receives a given intensity will be selected. Compared to the predictive multiple sampling algorithm, the improved predictive sampling algorithm can reduce system access time and power dissipation. The new method provides full-frame adaptive exposure control: integration slots are automatically selected for each pixel according to the intensity they receive. By a desired dynamic range expansion factor, the image sensors achieve high dynamic range.

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