Single Image Super-Resolution by Learned Double Sparsity Dictionaries Combining Bootstrapping Method

Abstract

A novel single image super-resolution (SISR) method using learned double sparsity dictionaries combining bootstrapping method is proposed in this paper. The bootstrapping method we used is proposed by Zeyde et al. in [1], which uses the input low-resolution (LR) image (as high-resolution image) and its own scaled-down version (as LR image) as the training images. In our previous work [15], with the output image obtained by the bootstrapping method, two difference images can be computed and are used to learn a pair of dictionaries as proposed in [1]. In this paper, we further improve the SISR method by using four wavelet sub-bands of the two difference images as extra information when learning the sparse representation model. We use the K-singular value decomposition (K-SVD) method to obtain the dictionary and the orthogonal matching pursuit (OMP) method to derive the sparse representation coefficients. Comparative experimental results show that our proposed method perform better in terms of both visual effect and Peak Signal to Noise Ratio (PSNR) improvements.