Manifold regularized sparse support regression for single image super-resolution

In this paper, we present a novel single image super-resolution method. To simultaneously improve the resolution and perceptual image quality, we bring forward a practical solution combining manifold regularization and sparse support regression. The main contribution of this paper is twofold. Firstly, a mapping function from low resolution (LR) patches to high-resolution (HR) patches will be learned by a local regression algorithm called sparse support regression, which can be constructed from the support bases of the LR-HR dictionary. Secondly, we propose to preserve the geometrical structure of the image patch dictionary, which is critical for reducing the artifacts and obtaining better visual quality. Experimental results demonstrate that the proposed method produces high quality results both quantitatively and perceptually.