Image super-resolution via Kernel regression of sparse coefficients

In this paper, we present a sparse coding (SC) inspired method to reconstruct a high-resolution (HR) image from one single low-resolution (LR) image. Instead of restricting the coding coefficients of LR and HR image patches to be equal or linearly mapped, we introduce kernel regression to nonlinearly relate the coding coefficients of LR patches and those of corresponding HR ones in an implicit fashion. Meanwhile, principal component analysis (PCA) is employed to train independent dictionaries which can well express image geometrical structure and ensure image sparse property. Experimental results show that the proposed method can effectively reconstruct image details and outperforms state-of-the-art algorithms in both quantitative and visual comparisons.