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研究生: 蔡佳軒
Tsai, Jia-Siuan
論文名稱: Switching Bilateral Filtering with a Texture/Noise Detector for Universal Noise Removal
結合紋理與雜訊偵測切換式雙邊濾波器之通用雜訊消除演算法
指導教授: 邱瀞德
Chiu, Ching-Te
口試委員:
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2009
畢業學年度: 97
語文別: 英文
論文頁數: 38
中文關鍵詞: 雙邊濾波器影像復原高斯雜訊脈衝雜訊混合式雜訊非線性濾波器
外文關鍵詞: switch bilateral filter, image restoration, Gaussian noise, impulse noise, mixed noise, nonlinear filters
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  • 在這篇論文中,我們提出了一個嶄新的切換式雙邊濾波器,並且結合紋理與雜訊偵測能去除混合式的雜訊模型。基本上的概念是先偵測然後如果有雜訊便進行濾波,若沒有判斷沒有雜訊便不進行濾波處理。我們提出象限中間值排序向量(SQMV)架構的方法,它能提供許多重要的訊息,像是在目前處理的位置中是否存在任何影像邊緣或是細節的資訊。並且可以從這些資訊裡再從SQMV中確定出一個參考中間值來代表目前影像位置的特性。在雜訊偵測器中,參考中間值用來跟目前的像素比較,並且判定目前的像素點是一個脈衝雜訊、高斯雜訊或是一個沒有雜訊的點。切換式雙邊濾波器確實能夠有效去除高斯與脈衝這兩種雜訊模型。依照目前判斷出的雜訊模型,來切換雙邊濾波器中差值濾波器的部份。模擬結果顯示我們設計的雜訊偵測器對於這兩種雜訊模型不只有很高的雜訊偵測率,也有很高的雜訊分辨率。切換式雙邊濾波器能夠達到很高的峰值信噪比與極佳的影像結果,與其他大部分濾波器不同的是,我們設計的切換式雙邊濾波器,不僅能夠去除單一雜訊模型,也能夠很有效地處理混合性的雜訊模型,其一是胡椒鹽雜訊混合均勻脈衝雜訊,另一個是胡椒鹽雜訊混合高斯雜訊。


    ABSTRACT III CHAPTER 1 INTRODUCTION - 1 - CHAPTER 2 SQMV FOR NOISE DETECTION - 4 - 2.1 NOISE MODEL - 4 - 2.2 MOTIVATION OF THE NOISE DETECTION SCHEME - 4 - 2.3 DEFINITION OF SORTED QUADRANT MEDIAN VECTOR (SQMV) - 6 - 2.4 FEATURES OF SQMV - 8 - 2.5 NOISE IDENTIFICATION WITH THE CLUSTERS OF SQMV - 11 - CHAPTER 3 SWITCHING BILATERAL FILTER - 16 - 3.1 BILATERAL FILTER - 16 - 3.2 SWITCHING SCHEME - 17 - 3.3 NOISE DETECTOR DESIGN - 19 - 3.4 SWITCHING BILATERAL FILTER - 21 - CHAPTER 4 PROPOSED FILTER SIMULATION RESULTS - 23 - 4.1 IMPLEMENTATIONS AND TESTING PROCEDURE - 23 - 4.2 COMPARISON OF NOISE DETECTION - 23 - 4.3 IMAGE QUALITY - 26 - CHAPTER 5 CONCLUSION - 34 - REFERENCE - 35 -

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