Single image super-resolution incorporating example-based gradient profile estimation and weighted adaptive p-norm

Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from only one observed low-resolution (LR) image. It is a severely ill-posed problem that needs image priors to ensure a reliable HR estimation. Since different priors usually emphasize different aspects of image characteristics, it's a big challenge to impose a balanced-and-overall image property constraint on single image SR. In this paper, we cast single image SR into a maximum a posteriori optimization problem and combine two types of complementary priors to answer this challenge. One prior is a novel local gradient field prior derived from example-based gradient field estimation (EGFE) that focuses on recovering the sharpness of gradient profiles. It is good at enhancing the edge sharpness and restoring fine texture details. Whereas the other is a powerful non-local low rank prior implemented in a weighted adaptive p-norm model (WANM). By imposing 1 p penalties adaptive to regional saliency and weighted constraints, the WANM prior performs well in preserving edge smoothness and suppressing image noise and reconstruction artifacts. An improved Split Bregman Iteration method that adaptively attenuates the regularization strength is further developed to solve the proposed EGFE-WANM SR problem. Comprehensive experiments are conducted and the results show that the proposed EGFE-WANM SR method outperforms many state-of-the-art methods in both objective evaluations and subjective visual comparisons. (C) 2019 Elsevier B.V. All rights reserved.