臺灣博碩士論文加值系統

隨著科技的發展，數位媒體形態日益呈現出多元化格局，越來越多人依賴數位媒體以獲得資訊和知識。目前的視訊系統在擷取影像畫面時，常常容易受到雜訊的干擾，特別是在低亮度環境下，雜訊汙染程度更為嚴重。雜訊會導致視訊的顯示品質不佳，是因為其本身含有大量不必要的資訊存在，使得視訊影片的壓縮率無法提高。因此，雜訊衰減在視訊處理中是一件很重要的研究。

本論文提出一個適用於昏暗環境中之視訊雜訊衰減方法，此方法是分析畫面的像素特性，將整張影像分為四種類型像素，對每一種像素採用不同的濾波處理。本方法主要的優點在於，有效地濾除平滑區域的雜訊，並同時能保留住影像的邊緣資訊。實驗結果顯示，論文提出的方法在主觀與客觀量測角度上，都比習知的方法優越，能大幅地改善視訊的顯示品質，提升編解碼器的壓縮效率，且可保持系統即時處理之效能。

As science and technology developing, the digital media appearance increasingly presents diversified structure and form. People more and more depend on the digital media to acquire the information and knowledge. In practical video systems, the source image is easily interfered by noise during acquisition process, especially in a low light environment. Noise causes the significant degradation of video quality because the noise remains as large residual errors and results in poor compression efficiency. Therefore noise reduction is an important research area in video processing.

In this paper, we propose a video noise reduction method in low light environment. The basic idea is to analyze the picture characteristic of the image. Divide the image into four kinds of regions and filtering these regions in different ways. The main advantage of the technique is that it is able to reduce the noise variance in smooth areas but at the same time retains the sharpness of edges in object boundaries. Experimental results show that our method is superior in the objective evaluation and subjective evaluation of human looking to other previous methods. The filter can achieve higher video quality and improve compression ratio. This filter not only can reduce video noise but also keep real-time process.

摘 要 i
ABSTRACT ii
誌謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1研究動機與方向 2
1.2相關工作 4
1.3論文組織 5
第二章 雜訊簡介 6
2.1 CCD 影像感測器 6
2.2 CCD 感測元件雜訊 11
2.3數學雜訊模型 (Noise Model) 14
2.3.1 高斯雜訊（Gaussian Noise） 14
2.3.2 脈衝雜訊（Impulse Noise） 16
2.3.3 均勻雜訊（Uniform Noise） 17
2.3.4 波松雜訊（Poisson Noise） 18
2.4夜間雜訊 20
2.5影像平均提升信號雜訊比 22
第三章 相關視訊雜訊衰減方法介紹 27
3.1 The video alpha-trimmed mean filter 27
3.2 The K nearest neighbor image sequence filter 29
3.3 A novel content-adaptive video denoising filter 32
3.4 An adaptive temporal filter based on motion compensation for video noise reduction 37
3.5 Motion adaptive spatio-temporal gaussian noise reduction filter for double-shot images 41
第四章 低光度環境下之視訊畫面增強 49
4.1像素分類偵測 50
4.2靜止像素之雜訊濾除 56
4.3邊緣移動像素之雜訊濾除 59
4.4平滑移動像素之雜訊濾除 61
4.5強烈且孤立雜訊濾除 67
4.6影像增強 68
第五章 實驗結果與比較 70
5.1評估準則 70
5.2多個濾波器比較 71
5.3實驗測試樣本 72
5.4實驗數據 80
5.5 分析與比較 110
第六章 結論與未來展望 112
6.1本研究方法之評析 112
6.2未來發展方向 112
參考文獻 114
相關著作發表 118

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