Single Image Super-resolution with Non-local Means and Steering Kernel Regression
Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution (LR) image. The non-local prior takes advantages of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means (NLM) filter to learn a non-local prior and the steering kernel regression (SKR) to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability (MAP) framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.