臺灣博碩士論文加值系統

目前在數位影像超解析度的研究中主要可以分成兩類，分別是以單一影像為基礎的解析度提升，以及使用多張影像作為參考的解析度提升，本研究的方法是採取單一影像作為解析度提升的方式在進行的。在本論文進行解析度提升的研究過程中，我們分別導入了數位影像處理中應用於頻率域以及空間域的處理技術，像是在進行影像初步的解析度放大過程中，我們使用了離散餘弦轉換所特有的低頻區能量緊密特性作為影像解析度提升上的再取樣基礎。而在得到了解析度經過初步放大後的初始高解析度影像後，為了針對影像在經過頻率域處理後可能會產生的混疊效應以及使用低頻資訊作放大後所特有的高頻遺失效應，我們另外使用空間域上的技術來對影像作混疊修補及高頻補償的處理。如此一來，影像在經過頻率域的低頻放大以及空間域的高頻補償後，便能夠得到一幅高品質的高解析度影像作為輸出。本論文所提出的演算法不論是在肉眼的主觀認知上，或是鋒值訊噪比的評比數值上，均比常見的使用多項式插補作解析度放大的演算法的結果還要來得有更好的視覺效果以及顯著的評比數據的提升。

There are two main approach of the image super-resolution research in digital
image processing. one is based on the single image, and the other is used by multiple
images which inter-reference each other. In the two different approaches, this paper
is belong to the first one. In our research approach, we use the frequency and spatial
domain methodologies of digital image processing respectively. The first step to get
the initial high resolution image is through the energy compaction characteristics of
Discrete Cosine Transform. Though we may get some extra side effects such like
aliasing and high-frequency losing in those images by using the frequency domain
transform technic, hence we then use the spatial domain processing technics for
dealing with those side effects and at the same time doing high-frequency feedbacks
synchronically. After this ways of the second step processing that image passes
through the low-frequency resampling and high-frequency feedbacks, we shall and
will get a high quality and high resolution image as our approach output. The super-
resolution algorithm we proposed in this paper can clearly find not only the subjective
viewing quality, but also the objective value of PSNR results a satisfied improvement.
Finally, the approach of ours exactly have a good improvement from the existing
interpolation algorithm like Bicubic.

第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 全文架構 3
第二章 影像超解析度文獻探討 4
2.1 數位影像處理的領域 4
2.2 影像觀察模型 4
2.3 文獻回顧 6
2.3.1 多項式插補的方法 6
2.3.2 頻率域上求解的方法 9
2.3.3 機率統計的方法 10
2.3.4 迭代法 11
2.3.5 混合型方法 12
第三章 影像超解析度相關演算法 13
3.1 多項式內插填補演算法 13
3.1.1 最近鄰域插補 13
3.1.2 雙線性插補 14
3.1.3 雙立方插補 15
3.2 頻率域的影像處理技術 16
3.2.1 快速傅立葉轉換 16
3.2.2 離散餘弦轉換 17
3.3 空間域演算法介紹 19
3.3.1 空間濾波器原理 19
3.3.2 邊緣偵測原理 20
3.3.3 Canny 演算法 21
3.3.4 拉普拉斯演算法 22
3.3.5 高斯函數平滑濾波 23
3.4 色彩空間的轉換 25
3.4.1 RGB色彩空間 25
3.4.2 YCbCr色彩空間 27
3.5 影像品質評比 28
3.5.1 PSNR 28
3.5.2 SSIM 29
第四章 離散餘弦轉換搭配迭代反投影之超解析演算法 30
4.1 演算法流程 30
4.2 色彩空間的轉換處理 33
4.3 影像於頻率域的再取樣 34
4.4 影像邊緣的修補處理 38
4.4.1 低通濾波後的失真效應 38
4.4.2 直線邊緣的失真效應 39
4.4.3 邊緣方向偵測 40
4.4.4 邊緣線段的遍歷 41
4.5 影像迭代反投影 44
4.5.1 條件假設 44
4.5.2 演算法程序 45
4.5.3 高斯濾波器的設計 48
第五章 實驗結果與討論 49
5.1 實驗流程 49
5.2 實驗結果與討論 50
5.2.1 在不同轉換尺度下透過DCT於頻率域作放大的結果 50
5.2.2 使用DCT低頻係數在頻率域作放大的結果 54
5.2.3 執行影像邊緣最大遍歷演算法後的結果 57
5.2.4 初始HR影像在空間域使用IBP運算後的結果 62
5.2.5 本論文演算法作放大處理的最後結果 64
5.2.6 實驗影像放大4倍的結果 76
5.2.7 實驗影像放大8倍的結果 85
5.3 實驗耗費時間的差異討論 94
第六章 結論 97
6.1 研究成果 97
6.2 研究限制 98
6.3 未來展望 98
參考文獻 99

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