Coupled multiple dictionary learning based on edge sharpness for single-image super-resolution

In this article a new strategy for single-image super-resolution is proposed. A selective sparse coding strategy based on patch sharpness is assumed to be invariant for patch resolution. This sharpness criterion is used at training stage to classify image patches into different clusters. It is suggested that the use of coupled dictionary learning, with a mapping function can improve the representation quality. By this strategy clustered dictionaries are designed along with a mapping function for each cluster which can provide the coupling link between low-resolution and high-resolution image patches. During the reconstruction, image patch sharpness is used as a criterion for the selection of a clustered dictionary and the mapping function. The high-resolution patches are recovered by high-resolution cluster dictionary atoms and the mapping function with sparse representation coefficients from low resolution patches. The algorithm is tested over a set of benchmark images from different data sets. Peak-signal-to-noise ratio and structural-similarity-index measures indicate that the given algorithm is competitive in general with existing baseline algorithms. The proposed algorithm performs better for images with high-frequency components.