In this paper, we present a sparse coding (SC) inspired method to reconstruct a high-resolution (HR) image from one single low-resolution (LR) image.

ABSTRACT. In this paper, we present a sparse coding (SC) inspired method to reconstruct a high-resolution (HR) image from one single.

PDF | This paper presents a new approach to single-image superresolution, based upon sparse signal representation. Research on image statistics suggests.

Missing: Kernel | Show results with:Kernel

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.

Abstract—This paper present a new method based on co-sparse with learning paired dictionary. The new framework is consisted of three parts.

Abstract—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target ...

ABSTRACT. Quality of an image is associated with edge of the image. It is impor- tant to preserve the edge of the image while deriving high resolution.

A learning-based single image SR method is proposed in [5], and the high resolution image is obtained by using sparse regression and natural image priors.

Abstract—We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end.