Coupled fisher discrimination dictionary learning for single image super-resolution

Image Super-resolution (SR) reconstruction techniques based on sparse representation have attracted ever-increasing attentions in recent years, where the choice of over-complete dictionary is of prime important for reconstruction quality. However, most of the image SR methods based on sparse representation fail to consider the discrimination and the redundance of the dictionaries, which lead to obvious SR reconstruction artifacts. In this paper, we propose a novel image SR framework using coupled fisher discrimination dictionary learning (CFDDL). With CFDDL, a pair of discriminative dictionaries are first learned for the same class of high-resolution (HR) image patches and corresponding low-resolution (LR) image patches, respectively. Then, we utilize the identical sparse representation for the same class of HR and LR image patches, which can not only discover the inherent relationship between the HR and LR image patches but also enhance the computational efficiency. Extensive experiments compared with several other SR methods demonstrate the superiority of the proposed method in terms of subjective evaluation as well as objective evaluation.