Single-Image Super-Resolution Based on Compact KPCA Coding and Kernel Regression

In this letter, we propose a novel approach for single-image super-resolution (SR). Our method is based on the idea of learning a dictionary which can capture the high-order statistics of high-resolution (HR) images. It 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 adopted to learn such a dictionary. A compact solution is adopted to reduce the time complexity of learning and testing for KPCA. Meanwhile, kernel ridge regression is employed to connect the input low-resolution (LR) image patches with the HR coding coefficients. Experimental results show that the proposed method is effective and efficient in comparison with state-of-art algorithms.