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研究生:郭勝夫
研究生(外文):Kuo, Sheng Fu
論文名稱:影像縫補技術應用於以樣本為基礎的超解析度演算法之研究
論文名稱(外文):Image Quilting for Example-based Super Resolution
指導教授:廖文宏廖文宏引用關係
指導教授(外文):Liao, Wen Hung
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:102
語文別:中文
論文頁數:74
中文關鍵詞:超解析度補丁影像縫補紋理合成
外文關鍵詞:super resolutionpatchimage quiltingtexture synthesis
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  • 被引用被引用:1
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高解析度影像含有較多的像素資訊,所以可以呈現出比低解析度影像更多的細節內容與色調變化,提升影像解析度的技術一直是數位影像處理的重要研究課題。在本論文中,我們實作了以樣本為基礎的超解析度演算法(example-based super resolution),其主要是利用高解析度與低解析度影像在空間上相對應的高頻資訊作為樣本,用以估算出相對合理(plausible)的高解析度影像。在演算法中有兩個關鍵的因素會影響執行結果的品質,一個是補丁合成的方法,另一個則是訓練資料的選擇。我們嘗試將影像縫補(image quilting)的技術應用在補丁的紋理合成(texture synthesis)上,使得縫補的邊緣可以得到較佳的連續性。實驗結果顯示本論文所提出的方法對於增強影像解析度有良好的效果。另外,學習型的超解析度演算法具有資料導向的特性,針對訓練資料的多寡與多樣性對於執行結果的影響,我們也在本論文作進一步的探討。
High-resolution images contain a larger number of pixels, more detailed content and color variations than low-resolution ones. Image resolution enhancement has been an important research area in digital image processing. In this thesis, we developed an example-based super-resolution algorithm which utilizes a collection of reduced-resolution images and their corresponding high-resolution images as examples to guide the estimation of plausible high resolution images from low-resolution ones. Two factors in the algorithm will influence the quality of the output image. One is the method for patch synthesis and the other is the selection of training data. To obtain better continuity among the boundaries between neighboring patches, we apply image quilting technology to synthesize the patch textures. Experimental results show that the proposed method has good performance on sharpening images. In addition, since example-based super resolution is intrinsically data-driven, we will also investigate the influence of the amount and the diversity of the training data on the result.
第一章 緒論 1
1.1 研究動機與目的 1
1.2 論文架構 5
第二章 相關研究 6
2.1 影像內插法 7
2.1.1 最近相鄰內差法 7
2.1.2 雙線性內插法 8
2.1.3 雙立方內插法 9
2.2 以樣本為基礎的超解析度演算法 10
2.2.1 產生關聯模型的訓練資料庫 11
2.2.2 馬可夫網路模型 11
2.2.3 One-pass演算法 12
2.3 影像縫補應用於紋理合成與轉移 15
2.3.1 影像縫補流程 16
2.3.2 最小錯誤邊界分割 17
第三章 研究方法 19
3.1 訓練程序 19
3.1.1 關聯模型 20
3.1.2 模糊化與重新取樣 21
3.1.3 高頻濾波 22
3.1.4 影像切割與局部對比正規化 23
3.2 超解析度程序 24
3.2.1 影像前處理 24
3.2.2 影像分割與局部對比正規化 25
3.2.3 資料搜尋與局部對比正規化的反向運算 26
3.2.4 影像縫補 27
第四章 實驗結果與分析 33
4.1 實驗環境 33
4.1.1 影像資料庫 33
4.1.2 影像評量方法 39
4.2 實驗結果 40
4.2.1 受測影像的取樣比率對於超解析度結果的影響 41
4.2.2 不同補丁合成方法對於超解析度結果的比較 45
4.3 訓練資料的特性對於超解析度結果的影響 54
4.3.1 不同數量的訓練資料對於超解析度結果的比較 54
4.3.2 不同性別的訓練資料對於超解析度結果的比較 56
4.3.3 不同人種的訓練資料對於超解析度結果的比較 61
4.3.4 不同類別的訓練資料對於超解析度結果的比較 66
第五章 結論與未來方向 70

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