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研究生:黃才荏
研究生(外文):Cai-Ren Huang
論文名稱:以降維分析法進行影像超解析之研究
論文名稱(外文):The Study of Dimension Reduction Methods for Image Super-resolution
指導教授:王元凱王元凱引用關係
指導教授(外文):Yuan-Kai Wang
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:43
中文關鍵詞:超解析降維方法視訊監控
外文關鍵詞:Super-resolutionDimension ReductionVideo Surveillance
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影像超解析技術是一個非常重要的方法,它能將低解析度影像還原成清晰的高解析度影像。本論文提出一個以非線性降維為基礎的超解析法,此超解析法使用的降維方法為two-directional two-dimensional locality preserving projection ((2D)2-LPP)。(2D)2-LPP為本論文提出之新的降維方法,其採用流形學習(Manifold learning),利用內部流形結構把高維空間投影到低維空間,並且不失去重要的資料。(2D)2-LPP可解決流形學習法中非樣本資料的問題(out-of-sample problem),改進新資料降維的速度。此外,(2D)2-LPP針對二維影像降維,能比PCA、LPP等方法保留更多資料。實驗資料從AR和FERET資料庫選取出適當的人臉。實驗結果表示本論文提出之方法比PCA、2D-PCA、(2D)2-PCA以及2D-LPP超解析的方法有更佳的PSNR。
Super-resolution is an important method to reconstruct high-resolution images from low-resolution images. In this paper, a nonlinear dimension reduction algorithm based on two-directional two-dimensional locality preserving projection ((2D)2-LPP) is proposed for face image super-resolution. The (2D)2-LPP is a new manifold learning method proposed by this paper. It detects the intrinsic manifold structure of high space and preserves the structure in low space by projection. The projection approach in the (2D)2-LPP resolves the out-of-sample problem in embedding-based manifold learning methods, and improves the speed in reducing the dimension of a new sample data. Moreover, the (2D)2-LPP preserves more accurate manifold structure by directly operating on 2D images rather than operating flattened 1D vector as PCA and LPP do. Extensive experiments are conducted on the AR and FERET databases. Experimental results show that the proposed method performs better than PCA,2D-PCA,(2D)2-PCA, and 2D-LPP super-resolution methods in PSNR.
Chapter 1 Introduction ...........................................1
1.1 Background..........................................................1
1.2 Motivation..........................................................3
1.3 Brief introduction of dimension reduction...........................................................4
1.3.1 PCA, 2D-PCA ,and (2D)2-PCA.................................................................5
1.3.2 LPP and 2D-LPP.................................................................8
1.4 The organization of this paper..............................................................10
Chapter 2 The Framework of Dimension Reduction-based Super-resolution...............11
2.1 Super-resolution by dimension reduction..........................................................11
2.2 PCA-based super-resolution.........................................................16
2.3 2D-PCA and 2D-LPP based super-resolution......................................................17
Chapter 3 (2D)2 Locality Preserving Projection for Super-resolution...........................19
3.1 Learning the super-resolution manifold by (2D)2-LPP........................................20
3.2 Dimension expansion for super-resolving images...............................................23
3.3 Algorithms for learning and super-resolution......................................................25
Chapter 4 Experimental Results............................................................27
4.1 Super-resolution results............................................................28
4.2 Cumulative percentage of variance...........................................................30
4.3 Regularization parameter λ..................................................................31
4.4 Training Number.............................................................32
4.5 Noise Robustness.........................................................34
4.6 Compare a low-resolution image and a feature to a high-resolution image.........35
Chapter 5 Conclusions and Future Work...............................................................37
References.........................................................38
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