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研究生:吳政鴻
研究生(外文):Jheng-Hong Wu
論文名稱:應用於影像雜訊消除之空間變化收斂參數型適應性濾波器研究與改進
論文名稱(外文):Research and Improvement of Adaptive Filters in SVCF for Image Noise Cancellation
指導教授:陳春僥黃崇禧
指導教授(外文):Chuen-Yau ChenChorng-Sii Hwang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系碩士班
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:2009
畢業學年度:97
語文別:中文
論文頁數:59
中文關鍵詞:適應性演算法雜訊濾除PSNR二維區塊適應性演算法
外文關鍵詞:PSNR2D block adaptive algorithmsadaptive algorithmnoise cancellation
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本論文針對TDOBA演算法提出了三種輔助收斂之機制,分別為MTDOBA-Type I演算法、MTDOBA-Type II演算法、MTDOBA-Type III演算法。我們取用高斯白雜訊作為參考輸入信號,並分別以四種區塊尺寸大小對本論文所提出之MTDOBA-Type I演算法、MTDOBA-Type II演算法、MTDOBA-Type III演算法進行模擬,最後針對輸出影像不平滑之問題提出改良式區塊掃描方式,取用高斯白雜訊作為參考輸入信號,進行雜訊濾除模擬試驗。模擬的結果證實我們所提出之MTDOBA-Type II演算法、MTDOBA-Type III演算法,可以穩定地濾除高斯白雜訊;改良式區塊掃描方式模擬結果顯示,可以有效提升雜除濾除之能力,並改善了輸出影像不平滑之問題。
In this thesis, we propose three mechanisms for improving the convergence rate in TDOBA. They are called MTDOBA-Type I, MTDOBA-Type II, and MTDOBA-Type III. In order to verify the efficiencies of these algorithms, we apply the Gaussian-white noise to be the input signal for the algorithms. Besides, there are four different block sizes applied in the processing. We also propose a new block-scanning scheme for improving the smoothness of the output signal. The simulation results show that the proposed MTDOBA-Type II and MTDOBA-Type III can reduce the Gaussian-white noise steadily. The proposed new block-scanning scheme does improve the signal-to-noise ratio and improve the smoothness of the output signal.
中文摘要...............................................................i
英文摘要...............................................................ii
誌謝...................................................................iii
目錄...................................................................iv
表目錄.................................................................iv
圖目錄.................................................................v
第一章 緒論..........................................................1
1.1 研究動機......................................................1
1.2 研究背景......................................................1
1.3 論文架構......................................................5
第二章 適應性演算法之介紹............................................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
2.3 區塊式影像處理................................................12
2.4 空間變化收斂參數型適應性演算法................................13
2.4.1 二維最佳區塊適應性演算法.............................14
2.4.2 二維最佳區塊隨機梯度演算法...........................16
2.4.3 混合型最佳區塊及混合型最佳區塊隨機梯度之適應性演算法..........18
第三章 空間變化收斂參數型適應性演算法之設計..........................22
3.1 雜訊模型介紹..................................................22
3.1.1 高斯白雜訊...........................................22
3.1.2 SVCF適應性演算法之高斯白雜訊濾除結果.................24
3.2 二維最佳區塊適應性演算法之改良................................25
3.2.1 改良式二維最佳區塊適應性演算法第一型[MTDOBA-Type I].......25
3.2.2 改良式二維最佳區塊適應性演算法第二型[MTDOBA-Type II]......29
3.2.3 改良式二維最佳區塊適應性演算法第三型[MTDOBA-Type III].....33
3.2.4 改良型SVCF之運算複雜度...............................36
第四章 模擬結果與比較................................................38
4.1 改良式TDOBA演算法之高斯白雜訊濾除模擬結果.....................39
4.2 區塊掃描方式..................................................43
4.2.1 改良式區塊掃描方式第一型.............................44
4.2.2 改良式區塊掃描方式第二型.............................45
4.2.3 改良式區塊掃描方式之模擬結果.........................46
第五章 結論與未來研究方向............................................52
5.1 結論..........................................................52
5.2 未來研究方向..................................................52
參考文獻 ..............................................................55
作者簡歷 ..............................................................59
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