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.