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研究生:李權益
研究生(外文):Chuan-I,Li
論文名稱:混合雜訊影像之自適應濾除
論文名稱(外文):An Adaptive Filtering Method for Mixed Noise of Color Images:A General Approach
指導教授:蘇德仁蘇德仁引用關係廖斌毅
指導教授(外文):Te-Jen SuBin-Yih Liao
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:資訊工程系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:100
畢業學年度:100
語文別:中文
論文頁數:94
中文關鍵詞:中值濾波器彩色影像高強度椒鹽雜訊彩色影像混合雜訊相似度
外文關鍵詞:Median Filterhigh-intensity Salt-and-Pepper noisecolor imagemixed noisesimilarity
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影像在傳輸的過程中,往往會受到許多因素的干擾而產生不同的雜訊,雜訊會使得影像分析結果產生誤判、而得到錯誤的資訊、或較差的影像品質,為了降低雜訊對於影像的影響,最簡單且容易實作的方式就是重新截取或傳輸影像資料,但這卻會耗費極大的時間成本,若該影像資訊的獲得極為困難,該方式則更不可行。
影像擷取過程中,普遍存在著雜訊(如:ISO雜訊、脈衝雜訊、高斯雜訊…),對於不同的雜訊有著不同適合的濾波器,在大部份的研究中,都使用一個特定的濾除方法,去濾除一個特定的雜訊。不幸地,影像通常不會只存在一種雜訊。現實中,影像可能混合著各種不同程度的雜訊。本論文討論如何處理影像中的混合雜訊並符合一般性。對於脈衝雜訊,中值濾波器有較佳且明顯的成效;對於高斯雜訊,均值濾波器在低標準差下有不錯的表現,多次取樣再平均也能有效且快速地做高斯雜訊濾除。本篇藉由幾個基本的雜訊濾波器,設計一個更佳的影像雜訊濾除方法。
演算法設計理念基於中值濾波器的想法,先濾除極值雜訊,根據濾鏡範圍內的極值像素,給予不同的濾鏡大小,再利用濾鏡內的像素值與像素平均值做相似度計算,依比例原則做權重調整,來達成高強度椒鹽雜訊及混合雜訊之彩色影像濾除。
Images are often affected by different noise interference through the transmission process. The noise causes error information in image analysis or brings quality of images. To reduce the influence of noise, a simple and easy method is to receive a fewer images and averages those images to get a clean image, but this method costs huge time. And if it is difficult to obtain the image, the method is more infeasible to implement.
Noise always exists anywhere, for example ISO noise, impulse noise, Gaussian noise and so on. Almost all of proposed methods used the specific filter to conquer the specific noise, however, there are more than one kind of noises existing in the image, i.e., the images are always mixed noises. Here we discuss how to handle the mixed noise in images for general. The median filter has better performance for impulse noise and the average filter is good at lower Gaussian noise respectively. In this thesis, proposed method is based on basic noise filters to design a better filter.
This paper presents a method based on median filter to avoid extreme value and designs an adaptive mask according to the number of extreme values in original mask. Then, finding the similarity for each pixel in the mask and giving weights to each pixel in the mask by the principle of proportionality. To do so, the presented method can achieve the goal which can remove high-intensity impulse noise and reduce mixed noised interference.
中文摘要 I
英文摘要 II
誌 謝 III
目 錄 IV
圖 目 錄 VI
表 目 錄 VIII
符 號 說 明 IX
第一章 緒論 1
1.1 研究動機 3
1.2 研究目的 3
1.3 論文章節規劃 4
第二章 相關研究 5
2.1 雜訊簡介 6
2.1.1 椒鹽雜訊 10
2.1.2 脈衝雜訊 11
2.1.3 高斯雜訊 12
2.2 濾波器 14
2.2.1 均值濾波器 14
2.2.2 中值濾波器 16
2.2.3 邊緣保留濾波器 18
2.3 影像修復 19
2.4 文獻歸納 22
第三章 理論架構 23
3.1 基本定義 24
3.2 去除極值 25
3.3自適應遮罩之調整規則 26
3.4 計算平均值與標準差 27
3.5 計算相似度 29
3.6 實際運作情況 30
3.7 處理流程圖 33
第四章 架構設計與分析 34
4.1虛擬碼 35
4.1.1 主程式虛擬碼 35
4.1.2 區段程式虛擬碼 36
4.2 時間複雜度分析 39
4.2.1 平均濾波器之時間複雜度 39
4.2.2 中值濾波器之時間複雜度 39
4.2.3 邊緣保留濾波器之時間複雜度 44
4.2.4本方法之時間複雜度 45
4.3 方法總比較 46
第五章 實驗結果與討論 47
5.1 實驗方法及目的 48
5.2 比較方法與測試平台 48
5.3 實驗結果與比較 49
5.3.1簡單測試 49
5.3.2椒鹽雜訊 50
5.3.3脈衝雜訊 55
5.3.4高斯雜訊 62
5.3.5混合雜訊 65
5.4 其他結果展示 73
5.5 結果分析 79
第六章 結論與未來展望 81
參考文獻 82
附 錄 - 各方法處理結果 88
附1 Marilyn Monroe-均值濾波器(3×3、5×5) 89
附2 Marilyn Monroe-中值濾波器(3×3、5×5) 90
附3 Marilyn Monroe-使用PhotoImpact邊緣保留模糊 91
附4 Marilyn Monroe-使用PhotoShop智慧型模糊 92
附5 Marilyn Monroe-使用PhotoShop汙點修復筆刷 93
附6 Marilyn Monroe -使用本篇方法 94
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