臺灣博碩士論文加值系統

本論文提出了四種應用於混合式雜訊消除之適應性濾波器，其中包含了穩定收斂區塊最小均方演算法與權重預調型穩定收斂適應性濾波器三型。在模擬的過程中，我們以四種影像區塊尺寸對本論文所提出之適應性演算法作驗證，並與五種現有的適應性演算法作比較。模擬的結果證實我們所提出之適應性演算法，可有效消除高斯與脈衝雜訊並具有較佳的穩定性。本論文所提之穩定收斂區塊最小均方演算法相較於二維區塊最小均方演算法，可具有較佳的濾除效果，PSNR值是原始演算法的1.04倍至2倍。此外，本論文所改良之權重預調型穩定收斂適應性濾波器相較於傳統預調型適應性濾波器，PSNR值是原始演算法的1.06倍至2.42倍。

In this thesis, we propose four adaptive filters for mixed noise cancellation in image signal processing. They are stable convergence block least-mean-square algorithm and three types of stable convergence adaptive filter with weight-training mechanism. During the simulations, we apply the proposed adaptive algorithms to deal with the images which are partitioned in four different block sizes. We also compare the simulation results with five existing adaptive algorithms. The simulation results show that the proposed adaptive algorithms are able to deal with Gaussian noise as well as pulse noise. The stable convergence block least-mean-square algorithm achieve up to twice PSNR as compared with that of the conventional two-dimensional block least-mean-square algorithm. The stable convergence adaptive filter with weight-training mechanism achieves the PSNR up to 2.42 times as compared with that of the adaptive filter with weight-training mechanism.

目 錄

中文摘要 .................................... i
英文摘要 ......................................ii
誌謝 .....................................iii
目錄 ......................................iv
表目錄 .................................... vi
圖目錄 .................................... vii
第一章 緒論................................ 1
1.1 研究動機...............................1
1.2 研究背景...............................2
1.3 論文架構...............................5
第二章 適應性演算法之回顧.....................6
2.1 適應性最小均方演算法...................6
2.1.1 隨機梯度法................. 6
2.1.2 最陡坡降法................. 8
2.1.3 最小均方演算法.............. 9
2.1.4 二維之最小均方適應性濾波器.. 10
2.2 應用於影像雜訊消除之二維適應性演算法..12
2.2.1 區塊式影像處理...............12
2.2.2 二維區塊最小均方演算法..... 13
2.3 快速收斂區塊最小均方演算法.......... 14
2.3.1 改良式快速收斂區塊最小均方演算法第一型...... 16
2.3.2 改良式快速收斂區塊最小均方演算法第二型............ 19
2.4 權重預調型適應性濾波器............................................................. 20
第三章 混合式雜訊消除之二維適應性演算法設計.......... 22
3.1 雜訊模型之介紹.................. 22
3.1.1 高斯雜訊....................22
3.1.2 脈衝雜訊.....................23
3.2 適應性演算法消除脈衝雜訊之分析...... 24
3.2.1 二維區塊最小均方演算法消除脈衝雜訊之分析........24
3.2.2 快速收斂區塊最小均方演算法消除脈衝雜訊之分析.... 26
3.2.3 改良式FCBLMS演算法第一型消除脈衝雜訊之分析......27
3.2.4 改良式FCBLMS演算法第二型消除脈衝雜訊之分析.... 29
3.2.5 權重預調型適應性濾波器消除脈衝雜訊之分析....... 30
3.3 濾除效果之原因探討.................. 31
3.4 二維區塊適應性演算法之改良.......... 32
3.4.1 穩定收斂區塊最小均方演算法...32
3.4.2 二維區塊適應性演算法對脈衝雜訊適應性之探討...... 36
3.4.3 快速收斂型適應性演算法處理混合式雜訊之比較......37
3.4.4 權重預調型穩定收斂適應性濾波器第一型............ 38
3.4.5 權重預調型穩定收斂適應性濾波器第二型........... 41
3.4.6 權重預調型穩定收斂適應性濾波器第三型............ 43
3.4.7 權重預調型適應性演算法處理混合式雜訊之比較..... 47
第四章 模擬結果與比較.................... 48
4.1 使用Lena影像於區塊尺寸設為4×4之模擬結果.......50
4.2 使用Lena影像於區塊尺寸設為8×8之模擬結果....... 54
4.3 使用Lena影像於區塊尺寸設為16×16之模擬結果...... 58
4.4 使用Lena影像於區塊尺寸設為32×32之模擬結果...... 62
4.5 使用Lena影像隨機測試100次之模擬結果數據總攬..... 66
4.6 各演算法於運算複雜度與效能之比較.... 78
第五章 二維適應性濾波器之硬體實現........... 82
5.1 穩定收斂區塊最小均方適應性濾波器之硬體實現...... 82
5.2 硬體模擬結果........................ 87
5.2.1 電路合成比較............... 87
5.2.2 演算法與硬體模擬比較. ..... 89
第六章 結論與未來研究方向.................. 93
6.1 結論................................ 93
6.2 未來研究方向........................ 93
參考文獻 .................................... 94
作者簡歷 .................................... 98

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