臺灣博碩士論文加值系統

本論文提出一個用於突波雜訊移除之適應性中值影像濾波器，此濾波器特別適用於高雜訊的影像，且因為運算複雜度低，故非常適合以硬體來實現。一般而言，突波雜訊移除濾波器分成兩個部分：突波雜訊偵測(Impulse Noise Detection)與影像濾波(Image Filtering)。我的方法主要是針對突波雜訊偵測做改善，使用在遮罩中最暗與最亮的點與其之間的絕對差值來做突波偵測與降低誤判情況。大部分突波雜訊偵測效果都會隨著影像的突波雜訊比率增高而降低，我所提出的突波雜訊偵測效果會隨著影像中的雜訊比率增高而更好。在影像濾波部分，則是使用自適性中值影像濾波(Adaptive Median Filtering)來將前級所測出的雜訊濾除，產生無雜訊的影像。
實驗數據顯示，此法的未偵測率、誤判率都優於其他演算法。最後，我也設計此濾波器的硬體電路，並結合Leon3 Soft CPU，實作在FPGA模擬板上，成為一個SoPC的系統。

In this thesis, I propose an adaptive rank-ordered median image filter for removing salt-and-pepper noise. Our method is suitable for images corrupted highly and it can be implemented easily with hardware due to its low computation complexity. Generally speaking, the image filter performs two functions: the first is the impulse noise detection, and the other is the image filtering. The main focus of this thesis is impulse noise detection. We use the brightest and lightest pixels in the mask to decide the current pixel as impulse noise or not, and use the absolute difference value of brightest and lightest pixels to decrease the error-detection. If the noise-ratio in the image increases, the performance of most impulse noise detection methods will decreases. However, the proposed method achieves better performance even the noise-ratio is very high. At the second stage, we use the adaptive median filtering to remove impulse noise by using the noise information obtained in the previous noise-detection stage.
Based on the experimental results, we found the performance of un-detection ratio and error-detection ratio of the proposed method is better than other algorithms. Finally, we develop the hardware circuit for the proposed filter. By integrating with the Leon3 soft-IP processor, we implement the whole system on a FPGA emulation board.

第一章 緒論 1
1.1研究背景 1
1.2研究動機與方向 2
1.3 論文組織 3
第二章 相關研究與文獻探討 4
2.1數位影像表示法 4
2.2影像濾波定義 (Image Filter Definition) 4
2.3失真的度量標準 5
2.3.1平均絕對值誤差 (Mean absolute error)： 6
2.3.2平均平方誤差 (Mean squared error)： 6
2.3.3訊號雜訊比 (Signal-to-noise ratio)： 6
2.3.4尖峰信號雜訊比 (Peak-signal-to-noise ratio，PSNR)： 7
2.4雜訊模組 (Noise Model) 8
2.4.1高斯雜訊 (Gaussian Noise) 8
2.4.2雷利雜訊 (Rayleigh Noise) 9
2.4.3指數雜訊 (Exponential Noise) 9
2.4.4均勻雜訊 (Uniform Noise) 10
2.4.5突波(胡椒鹽)雜訊 (Impulse Noise、Salt-and-Pepper Noise) 11
2.5一般突波移除演算法 12
2.5.1中值濾波器 (Median Filter) 12
2.5.2 自適性中值濾波器 (Adaptive Median Filter) 13
2.5.3 選擇性中值濾波器 (Switching Median Filter) 14
2.5.4累進式選擇中值濾波器 (Progressive Switching Median Filter) 16
2.5.5 自適性選擇中值濾波器 (Adaptive Switching Median Filter) 19
2.5.6新式突波雜訊偵測器 (New Impulse Detector for Switching Median Filter) 22
2.5.7排序順序突波雜訊偵測器 (Effective Impulse Detector Based on Rank-Order Criteria) 24
2.5.8三態式中值濾波器 (Tri-State Median Filter) 27
第三章 用於突波雜訊移除之適應性中值影像濾波器 29
3.1突波雜訊偵測 (Impulse Noise Detection) 30
3.1.1誤判情況討論 36
3.1.2未偵測情況討論 41
3.2 影像濾波 (Image Filtering) 45
第四章 突波雜訊移除之適應性中值影像濾波器實驗結果分析與評估 46
4.1未偵測率 (Un-Detected Ratio) 46
4.1.1使用經典影像：Lena 47
4.1.2 使用經典影像：Peppers 49
4.1.3使用經典影像Baboon 51
4.2誤判率 (Error-Detected Ratio) 54
4.2.1 使用經典影像 Lena 54
4.2.2 使用經典影像 Peppers 56
4.2.3 使用經典影像 Baboon 57
4.4 結論 68
4.4.1未偵測率實驗數據討論 68
4.4.2 誤判率實驗數據討論 69
4.4.3 PSNR實驗數據討論 71
第五章 使用Leon3與相關IP Cores建構出SOPC 73
5.1 Leon3 Processor與相關IP Cores 73
5.2 將本論文提出演算法實現於FPGA與執行結果 75
5.2.1 Leon3 SOC系統架構介紹 75
5.2.2 建構SOC所需要的相關軟體與硬體 76
5.2.3 設計突波雜訊偵測IP介紹 78
5.2.4 建構SOC(System On a Chip)流程 78
5.2.5 系統執行結果與畫面 79
參考文獻 83

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