Single Image super-resolution restoration algorithm from external example to internal self-similarity

Single image super-resolution (SR) restoration is an ill-posed inverse problem, in which regularization restriction is done with image priori knowledge. One single image SR method is proposed which simultaneously taking external example and internal self-similarity into account. Here the external knowledge is learned by convolutional neural network from external low-resolution-high-resolution image pairs, while the internal prior is utilized by cluster and low-rank approximation. The experimental results show that the proposed method outperforms many other existing super-resolution methods in recovery effect and robustness.