Coupled dictionary learning in wavelet domain for Single-Image Super-Resolution

In this paper a coupled dictionary learning mechanism with mapping function is proposed in the wavelet domain for the task of Single-Image Super-Resolution. Sparsity is used as the invariant feature for achieving super-resolution. Instead of using a single dictionary multiple compact dictionaries are proposed in the wavelet domain. Such dictionaries will exhibit the properties of the wavelet transform such as compactness, directionality and redundancy. Six pairs of dictionaries are designed using a coupled dictionary mechanism with mapping function which helps in strengthening the similarity between the sparse coefficients. Low-resolution image is assumed as the approximation image of the first-level wavelet decomposition. High resolution is achieved by estimating the wavelet sub-bands of this low-resolution image by dictionary learning and sparsity. The proposed algorithm outperforms a well-known spatial domain and wavelet domain algorithm as evaluated on the existing comparative parameters such as structural similarity index measure and peak signal-to-noise ratio.