中文摘要：

多尺度局部自相似性是指同一幅图像中存在相同尺度或不同尺度的相似子块，这种图像局部结构自相似性广泛存在于自然图像中。提出了一种基于多尺度局部自相似性结合邻域嵌入的单幅图像超分辨率算法，该算法不依赖于外界图像，仅仅在原始图像的局部子窗口中搜索目标图像块的相似子块，并结合邻域嵌入算法，进一步提高参与重建的图像块与目标图像块的相似性程度。实验结果表明，与双三次插值与传统邻域嵌入算法相比，新算法在保证算法效率的前提下，能有效提升超分辨图像的重建质量。

英文摘要：

Multi-scale local self-similarity, which widely occurs in natural images, refers to those similar patches either within the same scale or across different scales coming from the same input image. In this paper, we propose a single image super resolution algorithm based on multi-scale local self-similarity and neighbor embedding; this al- gorithm does not rely on an external example database nor use the whole input image as a source for example pat- ches. Instead, we extract patches from extremely localized regions in the input image and combine with neighbor embedding algorithm, further increasing the similarity between the patches which take part in reconstruction on the one hand, and the target patch on the other. Experimental results and their analysis demonstrate preliminarily that our method can improve the quality of super resolution image as compared with the bicubic interpolation and tradi- tional neighbor embedding algorithm, thus ensuring the efficiency of the algorithm.