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研究生: 李詩婷
Lee Shi-Ting
論文名稱: 基於影像復原之柵欄移除技術
A Fence Removing Scheme based on Image Inpainting
指導教授: 黃怡誠
Huang, Yi-Cheng
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 62
中文關鍵詞: 柵欄移除
英文關鍵詞: fence removing
論文種類: 學術論文
相關次數: 點閱:72下載:3
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  • 影像復原,是指將影像中被遮蔽物所遮蔽的區域,加以填補的動作。其主要目的是希望能復原出原先被遮蔽的影像資訊。此技術可應用在老舊照片折痕的修補、照片上文字的去除、欄杆的移除等。本研究即是提出了一個基於影像復原之柵欄移除技術,自動化填補未知區域的影像資訊,從一張靜態的數位影像上,移除前方的柵欄,顯現出柵欄後面的影像資訊。

    柵欄移除技術,主要是利用兩大步驟:邊緣偵測和漸進演算法。
    第一步驟,利用索貝爾(Sobel)的邊緣偵測特性,將影像中邊緣線找出來;接著利用柵欄的性質,對柵欄進行估測,刪除不是柵欄的邊緣及雜訊,以及修補斷掉的柵欄邊緣線,呈現出影像中柵欄的位置。
    第二步驟,對已判別出柵欄的區域進行移除,並修補原先被柵欄遮蔽的影像資訊,此方法藉著柵欄區周圍的資訊,進行顏色漸進的效果來達到復原影像。
    利用上面的兩大步驟,可以對單張靜態的數位影像,進行柵欄移除,並對原先被遮蔽的部分進行復原影像資訊,而呈現出無柵欄的完整影像。

    Image inpainting is to fill in the areas hiding from view. Its main purpose is to recover out the image information originally covered. The technology can be applied to old photos that have some creases, and get rid of characters, move fences, etc. on photos. Research here is a method based on the image inpainting technology that can remove the fence on a static photo. It can automatically fill in image information of unknown area. It can remove the fence in front of a piece of static image. Then it displays image information behind the fence.
    The technology of removing fence mainly utilize two major steps: detect the edge and a step by step color algorithm.
    The first step, it utilizes the characteristics on Sobel Edge Detection to find out the edge line in an image. Aftter that, it utilizes the nature of the fence to delete the edge and miscellaneous noise that is not belonged to the fence. It also repairs the fence edge line shed to break. Then it demonstrates the position of the fence of the image.
    Second step, it removes the area of fences that have already found. It recovers image information that covers by fence. The method use the step by step color algorithm to reach the image of inpainting by information around the fences.
    Utilizing two major steps above, it can move the fence from a single and static image and recover the information under the fence.

    摘 要 i ABSTRACT ii 第一章 緒論 1 第一節 研究動機與目的 1 第二節 本文架構 3 第二章  相關研究探討 4 第一節 傳統邊緣偵測方法 4 1. Prewitt 邊緣偵測 5 2. Sobel邊緣偵測 8 第二節 濾除雜訊的方法 10 1. 平均濾波 (Mean filter) 10 2. 中值濾波 (Median filter) 11 3. 最鄰近勻化法(Symmetric nearest neighbor filter, SNN) 12 第三節 影像的膨脹與侵蝕 15 第四節 影像修補的方法 17 1. 擴散式修補方法 17 2. 遮罩式修補方法 19 3. 結構式修補方法 20 第三章 研究方法 24 第一節 產生柵欄遮罩圖(Mask) 24 1. 手動產生遮罩 24 2. 自動產生遮罩 26 第二節 復原柵欄背後的影像 31 第四章 實驗步驟與結果分析 35 第一節 實驗系統環境 35 第二節 實驗與結果分析 36 1. 實驗---自動產生柵欄mask 36 2. 實驗---手動產生柵欄mask 40 3. 結果分析 44 第五章 總結和未來展望 52 第一節 總結 52 第二節 未來展望 54 參考文獻 55 附錄:去除柵欄圖 60 附圖目錄 圖 1(a):有柵欄圖 2 圖 1(b):有柵欄圖 2 圖 2 :邊緣解說圖 4 圖 3 :遮罩解說圖 6 圖 4(a):Prewitt原始圖 7 圖 4(b):Prewitt後的邊緣圖 7 圖 5(a):Sobel原始圖 9 圖 5(b):Sobel後的邊緣圖 9 圖 6 :中間濾波解說圖 11 圖 7 :SNN filter 13 圖 8 :膨脹圖 15 圖 9 :侵蝕圖 16 圖10 :擴散式修補方法 17 圖11(a):擴散式修補前原始圖 18 圖11(b):擴散式修補後的圖 18 圖12 :遮罩式修補前後圖 20 圖13(a):結構式修補法步驟1 21 圖13(b):結構式修補法步驟2 21 圖13(c):結構式修補法步驟3 22 圖14(a):結構式修補法前原始圖 22 圖14(b):結構式修補法中間過程圖 23 圖15 :原始柵欄圖 25 圖16 :程式中手動畫出柵欄遮罩(白色) 25 圖17(a):原始圖 27 圖17(b):Sobel後的圖 27 圖17(c):膨脹後的圖 28 圖17(d):侵蝕與去雜質後的圖 28 圖18(a):原始圖 29 圖18(b):Sobel X方向 29 圖18(c):膨脹動作 30 圖18(d):侵蝕+去雜質動作 30 圖19 :程式演算法簡圖 31 圖20(a):原始圖 33 圖20(b):復原後的影像 33 圖21(a):背景複雜原始圖 45 圖21(b):Sobel後邊緣圖 45 圖21(c):noise去除的邊緣圖 46 圖21(d):膨脹後的邊緣圖 46 圖21(e):侵蝕後的邊緣圖 47 圖21(f):復原後圖 47 圖22(a):手動mask圖 48 圖22(b):復原後的圖 48 圖23 :自動找出柵欄 50 圖23(a):去除柵欄(自動mask) 50 圖23(b):去除柵欄(手動mask) 50 圖24 :自動找出柵欄 51 圖24(a):去除柵欄(自動mask) 51 圖24(b):去除柵欄(手動mask) 51

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