影像超解析技術主要的目的在於由一張低解析度的影像重建出一張高解析度的影像，並且使得此高解析影像看起來清晰且自然。在本論文中，提出一種新的基於小波的超解析演算法，而它是基於在低解析度影像是高解析度影像的近似子頻帶並且試圖去估算未知細節係數的想法上，去建構一張高解析度的影像。為了降低傳統基於小波超解析演算法的缺點，例如鋸齒和振鈴現象，本論文採用一個有效的方向性內插，盡可能的保持正確的邊緣。此外利用簡單線性回歸以及一對低解析度與高解析度影像之間的關係來建立重構模組。運用重構模組來估算高頻子頻帶中的小波係數，並且產生高解析度的影像。從實驗結果可以很明顯的發現提出的演算法提供較好的效能。

The main objective of image super resolution technology is to reconstruct a high-resolution image from a low-resolution image, and make the high-resolution image become clear and natural. In this Thesis, a new wavelet based super resolution algorithm is proposed, and it is based on the idea that the low-resolution image is the low frequency subband of a higher resolution image and the high frequency subbands are estimated to reconstruct the high-resolution image. To reduce the drawbacks of traditional wavelet based super resolution algorithms, such as jaggy artifacts and ringing artifacts, an effective directional interpolation is adopted to accurately preserve the edges. Furthermore, simple linear regression is used in conjunction with the relationship of a pair of low-resolution and high-resolution images to build a reconstruction model. The coefficients in high frequency subbands are estimated by the reconstruction models, and a high-resolution image is generated. From the experimental results, it is clear that the proposed algorithm provides better performance.

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