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研究生:陳鵬宇
研究生(外文):Peng-YuChen
論文名稱:利用區塊結構比對之超解析演算法
論文名稱(外文):A Super-Resolution Algorithm using Patch Structure Matching
指導教授:戴顯權戴顯權引用關係
指導教授(外文):Shen-Chuan Tai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:2013
畢業學年度:101
語文別:英文
論文頁數:53
中文關鍵詞:超解析向量量化區塊結構
外文關鍵詞:super-resolutionvector quantizationpatch structure
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影像超解析技術的主要目的是利用低解析度的影像產生出高解析度的影像。在本論文中,一個以向量量化為基礎的超解析演算法被提出來做高解析度影像的產生。首先利用簡單的內插方法產生出一張初始模糊的高解析度影像,接著利用事先訓練好的編碼簿尋找比對產生出最合適的高頻資訊影像。最後將初始模糊影像與高頻資訊影像結合得到最終放大影像。為了準確的預測出高頻資訊,區塊結構比對的方法被提出來應用在編碼簿搜尋的階段。除此之外,LBG訓練演算法也被做修正來適應區塊結構比對的搜尋方法。實驗結果呈現了所提出的演算法具有比較良好的視覺品質。
The main purpose of super resolution technology is to generate high-resolution (HR) images from low-resolution (LR) images. In this thesis, a vector quantization (VQ) based super resolution algorithm is proposed to produce HR images. Firstly, the initial blurred HR images are generated by a simple interpolation method. Furthermore, the high-frequency information images are obtained by searching the pre-trained codebook to find the best matching codevector. The final enlarged images are generated by combining the initial blurred images and the high-frequency information images. In order to predict the high-frequency information accurately, a patch structure matching method is proposed in the codebook searching phase. Besides, LBG training algorithm is also modified to adapt to the patch structure matching. Experimental results show that the proposed algorithm produces HR images with better visual quality.
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . .ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . iii
Table of Contents . . . . . . . . . . . . . . . . . . . . iv
List of Tables . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . vii
1 Introduction . . . . . . . . . . . . . . . . . . . . . .1
1.1 Overview of Super-Resolution . . . . . . . . . . . . .1
1.2 Organization of This Thesis . . . . . . . . . . . . . 4
2 Background and Related Works . . . . . . . . . . . . . .5
2.1 Vector Quantization . . . . . . . . . . . . . . . . . 5
2.1.1 Vector Quantization for Super-Resolution . . . . . 6
2.1.2 Classied Vector Quantization . . . . . . . . . . . 6
2.1.3 Interpolative Vector Quantization . . . . . . . . . 12
2.2 Super-Resolution Only with HR Codebook . . . . . . . .13
2.3 Image Interpolation Method . . . . . . . . . . . . . 16
2.3.1 Bicubic interpolation . . . . . . . . . . . . . . . 16
2.3.2 Lanczos interpolation . . . . . . . . . . . . . . . 17
3 The Proposed Algorithm . . . . . . . . . . . . . . . . 19
3.1 Super-Resolution Reconstruction . . . . . . . . . . . 19
3.1.1 Prediction of High-Frequency Information of the Texture Class . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Prediction of High-Frequency Information of the Strong Edge Class . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Codebook Design . . . . . . . . . . . . . . . . . . . 27
3.2.1 Codebook Training of Texture . . . . . . . . . . . .28
3.2.2 Codebook Training of Strong Edge . . . . . . . . . .30
4 Experimental Results . . . . . . . . . . . . . . . . . 32
5 Conclusions and Future Works . . . . . . . . . . . . . .49
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . 49
5.2 Future Works . . . . . . . . . . . . . . . . . . . . 49
References . . . . . . . . . . . . . . . . . . . . . . . .51
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