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 high-resolution images based on …

This paper proposes a regression-based method for single-image super-resolution. Kernel
ridge regression (KRR) is used to estimate the high-frequency details of the underlying high …

The goal of single-image super-resolution is to generate a high-quality high-resolution
image based on a given low-resolution input. It is an ill-posed problem which requires …

We propose a fast regression model for practical single image super-resolution based on in-
place examples, by leveraging two fundamental super-resolution approaches-learning from …

In this paper we address the problem of producing a high-resolution image from a single low-
resolution image without any external training set. We propose a framework for both …

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 …

Nearest neighbor-based algorithms are popular in example-based super-resolution from a
single image. The core idea behind such algorithms is that similar images are close in the …

This paper presents a novel example-based single-image superresolution procedure that
upscales to high-resolution (HR) a given low-resolution (LR) input image without relying on …

In this paper, we propose a novel algorithm for fast single image super-resolution based on
self-example learning and sparse representation. We propose an efficient implementation …

In this paper, we propose a learning-based super resolution approach consisting of two
steps. The first step uses the kernel partial least squares (KPLS) method to implement the …