Learning Based Single Image Super Resolution Using Discrete Wavelet Transform

Sparse representation has attracted considerable attention in image restoration field recently. In this paper, we study the implementation of sparse representation on single-image super resolution problem. In recent research, first and second-order derivatives are always used as features for patches to be trained as dictionaries. In this paper, we proposed a novel single image super resolution algorithm based on sparse representation with considering the effect of significant features. Therefore, the super resolution problem is approached from the viewpoint of preservation of high frequency details using discrete wavelet transform. The dictionaries are constructed from the distinctive features using K-SVD dictionary training algorithm. The proposed algorithm was tested on 'Set14' dataset. The proposed algorithm recovers the edges better as well as improving the computational efficiency. The quantitative, visual results and experimental time comparisons show the superiority and competitiveness of the proposed method over the simplest techniques and state-of-art SR algorithm.