臺灣博碩士論文加值系統

本研究提出自適應性模糊加權平均濾波器是基於方向性中值濾波的技術及模糊推論而提出，用於去除數位影像中的隨機值脈衝雜訊。為了能夠實現於隨機值脈衝雜訊中的邊緣與紋理保留以及於高雜訊有更好的修復能力，本研究主要想法是從濾波視窗中四個主要的方向取得最可靠的方向的資訊，並以該方向的中值為基準來評估濾波視窗中每一個像素的重要性。另外，利用該中值與濾波視窗中的標準差，動態地建構模糊推論中的模糊歸屬函數，再利用模糊推論計算出濾波視窗所對應的權重視窗。因此，權重視窗裡的權重值代表著濾波視窗中每個像素被參考的重要性，以權重視窗與濾波視窗利用加權平均的方式以獲得取代雜訊像素值的修復像素值。此新的技術於圖片擁有高過一半之高隨機值脈衝雜訊密度時，改進了在邊緣保留的性能以及有更好修復圖片的效果。本研究大量的實作結果顯示出於高雜訊時，於視覺上與PSNR值的效能分別比其他濾波方法有更好的表現。

This study proposes adaptive fuzzy weighted mean filter which is based on directional median technique and fuzzy inference for random noise values in Image processing. The main idea of this study is not only able to get information from each direction of the filtering window, but also it is able to obtain information from every pixel of filtering windows. Thus, in order to implement preserving details and textures, and better restoration in high noise case, this study utilize the directional median to build member function in fuzzy inference dynamically. Then, using fuzzy inference to calculate the weighted window corresponding to filtering window. Weights of the weighted window represents the importance of pixels, which means how much pixel values should be provided. Finally, the restoration pixel is calculated by the weighted window and the filtering window using weighted mean. This new technique improves performances of preserving detail and fixed image in noise over 50%. In extensive experiments of this study show performances of PSNRs and visual tests better than others filter methods.

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 IX
第一章 緒論 1
第一節 研究動機 1
第二節 研究背景 1
第三節 論文架構 3
第二章 相關技術介紹 4
第一節 脈衝雜訊模型 4
第二節 ROR脈衝雜訊檢測演算法 4
第三節 模糊邏輯 6
第四節 模糊系統 13
第五節 稀疏排序 15
第三章 研究方法 18
第一節 迭代架構的新停止條件 19
第二節 方向性模糊加權平均濾波器 19
第三節 演算法 27
第四章 實驗結果及討論 29
第一節 影像復原與重建效能評估 30
第二節 影像復原與重建的視覺評估 34
第三節 迭代停止條件評估 61
第五章 結論及未來展望 70
第一節 結論 70
第二節 未來展望 71
參考文獻 72

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