Image super-resolution via non-local steering kernel regression regularization
In this paper, we employ the non-local steering kernel regression to construct an effective regularization term for the single image super-resolution problem. The proposed method seamlessly integrates the properties of local structural regularity and non-local self-similarity existing in natural images, and solves a least squares minimization problem for obtaining the desired high-resolution image. Extensive experimental results on both simulated and real low-resolution images demonstrate that the proposed method can restore compelling results with sharp edges and fine textures.