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研究生:謝靖慈
研究生(外文):Ching-Tzu Hsieh
論文名稱:使用雙向性濾波器實現彩色影像增強及削減視訊雜訊
論文名稱(外文):Color Image Enhancement and Video Noise reduction based on Bilateral Filter
指導教授:貝蘇章
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:105
中文關鍵詞:雙向性濾波器削減雜訊增強對比
外文關鍵詞:Bilateral filternoise reductioncontrast management
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當影像和視訊經由通道傳輸和天線的感應器接收時通常都很容易受到雜訊的干擾,而由於這些干擾往往都會降低這些多媒體資料的品質而使得人眼感到不舒服,因此我們提出了一些削減雜訊的演算法來降低雜訊對於影像和視訊的影響。然而,許多研究顯示出,由於影像和視訊受到雜訊和模糊的侷限,因此有效削減雜訊的方法現今仍然是一大挑戰。現今有許多提出的演算法當中,雖然再削減雜訊功能上有傑出的結果,然後卻無法避免去產生人為的產物例如造成影像的模糊,這些都是由於雜訊和影像中的細節部分都屬於高頻的成分,要分辨他們就更加的困難。
1998年Tomasi和Manduchi提出了一種雙向性濾波器的概念,這個濾波器結合了區域濾波器以及值域濾波器分別來計算像素之間的幾何距離和像素亮度之間的相似度,以此可以削除影像的雜訊同時保留了影像中的細節。傳統上,雙向性濾波器只有針對灰階影像削除雜訊,在這篇論文當中,我們將此概念延伸到彩色影像,理論上,因為考慮到顏色的失真,所以我們可以得到比較好的結果,在後面的章節,我們會展示實驗的結果,由實驗的結果可以說明我們所提出的演算法可以在去除雜訊同時仍然保留影像的邊緣。
雙向性濾波器被使用需用影像處理的應用上,除了削除影像雜訊以外,也可以用在處理影像的對比還有削減視訊雜訊。我們也會用2D的遮罩和3D的遮罩雙向性濾波器應用在削減視訊雜訊,當然,使用3D遮罩的雙向性濾波器有較好的結果並且不會產生鬼影。


Images and videos are usually corrupted by noise when they are transmitted through communication networks and received from the sensors of antenna. Since the interference reduces quality of the multimedia, we consider the algorithm for noise reduction and design it to decrease the influence of noise. However, the researches for image denoising method are still a hard challenge because it is difficult to distinguish between edge texture and noise component since both of them belong to high frequency component. Therefore, many proposed algorithms which have an outstanding performance in the noise reduction but it is easy fail in avoiding artifact.
Bilateral filter was proposed by C. Tomasi and R. Manduchi in 1998. The filter combines domain filter and range filter which measure geometric closeness and photometric similarity between pixels respectively to remove out noise for edge preserving in the spatial domain. Conventionally, bilateral filter is applied to denoise for grayscale image. This thesis extends the concept to color image for noise reduction. Theoretically, it has a better performance because it considered color distortion. The experiment shows the performance which successfully indicates our algorithm in noise reduction and preserving edge simultaneously.
Bilateral filter is applied to many applications for image processing. In addition to image noise reduction, it is also applied to contrast management of high dynamic image and video noise reduction. We implement the filter based on 2D mask and 3D mask to video noise reduction and compare their outcome. Typically, the volumetric bilateral has better performance and doesn’t produce artifact.


口試委員會審定書 #
誌謝 i
中文摘要 iii
ABSTRACT v
CONTENTS vii
LIST OF FIGURES xi
LIST OF TABLES xiii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Spatial domain methods 2
1.3 Quality Measure System 4
1.3.1 Signal Noise Ratio (SNR) 5
1.3.2 Structural SIMilarity (SSIM) index 6
1.4 Thesis Organization 9
Chapter 2 Edge-preserving Filtering with Bilateral Filter 11
2.1 Concept of Bilateral Filter (BF) 11
2.2 Bilateral filter using Gaussian Function 12
2.3 Modify type 17
2.3.1 Adaptive bilateral filter (ABF) 17
2.3.2 Bilateral Enhancers (BE) 20
2.4 Application 24
2.5 Conclusion 25
Chapter 3 Problem Setup 27
3.1 Parameter Selection 27
3.2 Fast technique 33
3.2.1 Exact Computation based on local histograms 34
3.2.2 Brute force 36
3.2.3 Separable kernel 36
3.2.4 Local histograms 38
3.2.5 Layered approximation 39
3.2.6 Bilateral grid 40
3.2.7 Compare the result and Conclusion 42
3.3 Conclusion 42
Chapter 4 Color Image Noise Reduction 45
4.1 Gray image noise reduction 46
4.2 Noise reduction using HSV color space 50
4.3 Noise reduction in RGB color space 57
4.4 Simulation result and conclusion 65
4.5 Conclusion 69
Chapter 5 Apply Bilateral Filter to High Dynamic Range Image 71
5.1 Contrast Management of Image 72
5.2 Image enhancement based on bilateral filter 73
5.2.1 Decompose into Coarse and Fine 73
5.2.2 Tone mapping 75
5.3 Simulation result 82
5.4 Conclusion 85
Chapter 6 Video Noise Reduction on Spatial Domain 87
6.1 2D Bilateral Kernel 88
6.1.1 Spatial bilateral filter 89
6.1.2 1D Bilateral kernel in Time 91
6.2 Combine spatial domain and temporal domain 92
6.3 Simulation result and Comparison 95
6.4 Conclusion 96
Chapter 7 Future Works and Conclusions 97
7.1 Thesis conclusion 97
7.2 Future work 98
REFERENCE 101



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