结合结构自相似性和卷积网络的单幅图像超分辨率

针对单幅图像超分辨率（SR）复原病态逆问题，在重建过程边缘细节丢失导致的模糊，提出一种结合结构自相似和卷积网络的单幅图像超分辨率算法。首先，通过将尺度分解获得待重构图片样本的自身结构相似性，结合外部数据库样本结合作为训练样本，可以解决样本过于分散的问题；其次，将样本输入卷积神经网络（CNN）进行训练学习，得到单幅图像超分辨率的先验知识；然后，利用非局部约束项自适应选择最优字典重建图像；最后，利用迭代反投影算法对图像超分辨率效果进一步提升。实验结果表明，与双三次插值（Bicubic）方法、K-SVD算法和基于卷积神经网络的图像超分辨率（SRCNN）方法等优秀算法相比，所提算法可以得到边缘更为清晰的超分辨率重建效果。

Single image super resolution combining with structural self-similarity and convolution networks

Aiming at the ill-posed inverse problem of single-image Super Resolution (SR) restoration, a single image super resolution algorithm combining with structural self-similarity and convolution networks was proposed. Firstly, the self-structure similarity of samples to be reconstructed was obtained by scaling decomposition. Combined with external database samples as training samples, the problem of over-dispersion of samples could be solved. Secondly, the sample was input into a Convolution Neural Network (CNN) for training and learning, and the prior knowledge of the super resolution of the single image was obtained. Then, the optimal dictionary was used to reconstruct the image by using a nonlocal constraint. Finally, an iterative backprojection algorithm was used to further improve the image super resolution effect. The experimental results show that compared with the excellent algorithms such as Bicubic, K-SVD (Singular Value Decomposition of k iterations) algorithm and Super-Resolution Convolution Neural Network (SRCNN) algorithm, the proposed algorithm can get super-resolution reconstruction with clearer edges.