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.

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