目前為了安全或保全的需求，攝影機已經逐漸安裝在車輛上，因此在本研究中，假設攝影機安裝在擋風玻璃的後方，下雨時在玻璃上的水珠會影響攝影機影像的清晰度，也容易造成以影像為基礎之相關功能的失常，如：車道偏離、前方碰撞警示、交通號誌(燈)的辨識等。因此，本研究的目標是移除使用行車影像紀錄器(DVR)所錄製之視訊影像上的水珠。
為了達成上述目標，本研究運用一些步驟，包含：直方圖等化、交集運算和二值化等來偵測水珠，接著形狀判斷以過濾水珠，這些偵測出的水珠將根據他們是位於畫面中的路面或街景區域，分別使用不同方法來進行修復與還原，這些方法包括樣本比對、形態運算以及圖像修補。
在實驗方面，我們使用實際預錄之視訊影像來驗證所設計方法的效能，從肉眼檢視下可以明顯地察覺到水珠被移除的效果，展示本論文提出之方法確實可以有效地移除視訊影像上的水珠。

Camera is widely installed in the vehicle for safety or security reasons. In this study, a camera is assumed to be installed behind the protection glass or windshield window. Water drops on the glass or window influence the clarity of the camera view. It easily causes the malfunction of related image-based functions, such as lane departure warning, front collision warning, traffic light or sign recognition, and so on. Therefore, the purpose of this study is the removal of water drops from the video frames which recorded from the in-vehicle digital video recorder (DVR).
In order to achieve the above purpose, water drops are detected by using several steps, including histogram equalization, frames intersection, binarization, and shape checking. Those detected water drop areas are restored by using different methods according to their locations in either the roadway or building area. These methods include template matching, morphological operation, and image inpainting.
Several videos are used to demonstrate the performance of the proposed method. Water drops can be removed obviously from the visual inspection. The results demonstrate that the proposed method is useful to remove water drops in video frames.

Outline
Abstract (Traditional Chinese) I
Abstract II
Table of Figures VI
Chapter 1 Introduction 1
Chapter 2 Related Work 5
Chapter 3 Method 12
3.1 Process 12
3.2 Water drop detection phase 13
3.3 Water drop removal phase 24
Chapter 4 Demonstration 31
4.1 Discussion 31
Chapter 5 Conclusion and Feature Work 36


List of Figures
Figure 1: The water drops influence the clarity of the camera view 2
Figure 2: A series of video frames with water drops 3
Figure 3: Some examples of shape representation 6
Figure 4: Flow diagram of the proposed algorithm 7
Figure 5: Lane detection results from an on-road sequence 7
Figure 6: The reducing and enhancing rain drops 8
Figure 7: A example of snow removal 9
Figure 8: Driver assistance in fog 9
Figure 9: Results of noise removal 10
Figure 10: Results of raindrop model and image restoration 11
Figure 11: The process of the proposed method 13
Figure 12: An example of traffic lane detection 16
Figure 13: The example of adaptive binarization which Pr is 9 also Pb is 0.05 21
Figure 14: Water drop detection steps 23
Figure 15: The example of shape/mean value checking 24
Figure 16: An example of template matching and replacement 27
Figure 17: The difference between morphological operation and image inpainting 29
Figure 18: An image inpainting replacement of a water drop covering the traffic lane 29
Figure 19: The water drop removal example with seldom number of water drops 34
Figure 20: A water drop removal example with medium number of water drops 34
Figure 21: A water drop removal example with large number of water drops 35

List of Table
Table 1: The prototype system of development environment 33
Table 2: The executing time of each phase 33

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