Single image super-resolution using regularization of non-local steering kernel regression

One promising technique for single image super-resolution (SR) is reconstruction-based framework, where the key issue is to apply reasonable prior knowledge to well pose the solution to upsampled images. In this paper, we employ the non-local steering kernel regression (NLSKR) model to devise an effective regularization term for solving single image SR problem. The proposed regularization term is based on the complementary properties of local structural regularity and non-local self-similarity existing in natural images, aiming at preserving sharp edges and producing fine details in the resultant image. By integrating the regularization term into the standard back-projection framework, we solve a least squares minimization problem to seek the desired high-resolution (HR) image. Extensive experimental results on several public databases indicate that the proposed method produces promising results in terms of both objective and subjective quality assessments. HighlightsThe non-local steering kernel regression (NLSKR) model is used to devise an effective regularization term for solving the single image SR problem.A new regularization term is designed for preserving sharp edges and producing fine details in the resultant image.By integrating the regularization term into the standard back-projection framework, we solve a least squares minimization problem to seek the desired high-resolution image.