基于多尺度结构自相似性的单幅图像超分辨率算法

多尺度结构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结构，这种多尺度图像结构自相似性广泛存在于遥感图像中.本文提出了一种基于多尺度结构自相似性的单幅图像超分辨率（Super resolution，SR）算法，该算法结合了压缩感知框架与图像结构自相似性，利用非局部方法和基于图像金字塔的K-SVD字典学习方法，将蕴含在相同尺度和不同尺度相似图像块中的附加信息在压缩感知的框架下加入到重构图像中.本文算法的优势在于，它仅借助于单幅低分辨率图像自身所蕴含的信息，实现了空间分辨率的提升.实验表明，与CSSS算法和ASDSAR算法相比，本文算法更有效地提升了遥感图像的空间分辨率.

Single Image Super Resolution Based on Multi-scale Structural Self-similarity

Multi-scale structural self-similarity refers to those similar structures either within the same scale or across different scales coming from the same image, which widely occur in remote sensing images. In this paper, we propose a single image super resolution (SR) method based on multi-scale structural self-similarity, which combines compressive sensing framework and structural self-similarity. In our method, the nonlocal and the pyramid-based K-SVD methods are used to add the extra information hidden in multi-scale structural self-similarity into the reconstructed image in the compressive sensing framework. The advantage of our method is that it only uses a single low-resolution image to promote spatial resolution by fully exploiting the extra information hidden in the image itself. Experimental results demonstrate that our method can improve spatial resolution more effectively compared with the CSSS and the ASDSAR methods.