Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

We call methods for achieving high-resolution enlargements of pixel-based images super-
resolution algorithms. Many applications in graphics or image processing could benefit from …

In this paper, we propose an image super-resolution approach using a novel generic image
prior-gradient profile prior, which is a parametric prior describing the shape and the …

In this paper we propose a new method for upsampling images which is capable of
generating sharp edges with reduced input-resolution grid-related artifacts. The method is …

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 …

Edge-directed image super resolution (SR) focuses on ways to remove edge artifacts in
upsampled images. Under large magnification, however, textured regions become blurred …

Image inpainting is the filling in of missing or damaged regions of images using information
from surrounding areas. We outline here the use of a model for binary inpainting based on …

This paper presents a new formulation of the regularized image up-sampling problem that
incorporates models of the image acquisition and display processes. We give a new analytic …

In this paper, we study face hallucination, or synthesizing a high-resolution face image from
an input low-resolution image, with the help of a large collection of other high-resolution face …

We introduce a class of inverse problem estimators computed by mixing adaptively a family
of linear estimators corresponding to different priors. Sparse mixing weights are calculated …