本論文針對Chang et al. 所提之基於鄰居內嵌法的超解析演算法，提出了一個改進的版本。取代Chang et al.採用歐式距離尋找K個最接近之鄰居之方法，我們定義一個相似度Similarity，來尋找K個最相似之鄰居。相似度定義內含有兩區塊內導數的標準差，因而能找出更適當的鄰居。除此，我們也改進線性組合係數的取法，進而有效地改進整個超解析之結果。

In this paper, we propose an improved version of the neighbor-based super-resolution algorithm proposed by Chang et al.. Different from Chang’s method that uses the Euclidean distance to find the K most nearest neighbors of a low-resolution patch, we define a similarity function and use it to find the K most similar neighbors of a low-resolution patch. In addition, we use a set of different weights for taking a linear combination of the high-resolution patches corresponding to the selected K most nearest neighbors. Although the set of the weights used by Chang minimizes the error they defined, the reconstructed high-resolution images by our method have better PSNR and SSIM than those constructed by Chang’s method.

目錄
第一章 緒論	1
1.1 簡介	1
1.2 動機與目的	3
1.3 論文架構	4
第二章 相關研究	5
2.1 動態超解析技術	5
2.2 靜態超解析技術	8
2.3 本論文採用之方法	11
第三章 相關基礎理論	12
3.1 YCbCr色彩空間	12
3.2 局部線性內嵌法 (Locally Linear Embedding, LLE)	13
3.3 PSNR	14
3.4 SSIM	15
第四章 研究方法	17
4.1 訓練樣本蒐集	17
4.2 超解析處理	19
第五章 實驗結果與分析	24
第六章 結論	40
參考文獻	41
附錄：英文論文	43

圖目錄
圖一 動態影像超解析技術示意圖[2]	6
圖二 IBP演算法示意圖[10]	7
圖三 雙立方內插說明圖	8
圖四 使用馬可夫網路模組示意圖[13]	9
圖五 利用SVR作超解析示意圖[17]	10
圖六 訓練樣本蒐集流程	17
圖七 一階與二階導數迴旋積計算罩（Convolution Mask）	19
圖八 超解析處理流程	20
圖九 訓練樣本包含10張人臉影像與10張一般影像	24
圖十 影像弱化過程	25
圖十一 對人臉影像作處理	26
圖十二 對愛因斯坦影像作處理	27
圖十三 愛因斯坦影像之局部放大圖	27
圖十四 對船影像作處理	28
圖十五 船影像的局部放大圖	28
圖十六 對城堡牆壁影像作處理	29
圖十七 對小孩影像作處理	30
圖十八 對Lena作處理	31
圖十九 Lena影像局部放大圖	31
圖二十 對熊影像作處理	32
圖二十一 熊影像的局部放大圖	32
圖二十二 對老人影像作處理	33
圖二十三 老人影像帽子局部放大圖	33
圖二十四 對海星影像作處理	34
圖二十五 海星影像局部放大圖	34
圖二十六 對神殿影像作處理	35
圖二十七 神殿影像地面局部放大圖	35
圖二十八 對自由女神影像作處理	36
圖二十九 自由女神影像耳朵局部放大圖	37
圖三十 對狗影像作處理	37
圖三十一 小狗影像頭的局部放大圖	38

表目錄
表1 各個方法的PSNR及SSIM值	39

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