Single image super-resolution via sparse KPCA and regression

In this paper, we present a new approach to single image super-resolution (SR). The basic idea is to learn a dictionary which can capture the high-order statistics of high-resolution (HR) images. This is of central importance in image SR application, since the high-order statistics play a significant role in the reconstruction of HR image structure. Kernel principal component analysis (KPCA) is used to learn such a dictionary. To reduce the time complexity of learning and testing for KPCA, a sparse solution is adopted. Meanwhile, kernel ridge regression is employed to relate the input low-resolution (LR) image patches and the HR coding coefficients. Experimental results show that the proposed method can effectively reconstruct image details and outperform state-of-the-art algorithms in both quantitative and visual comparisons.