Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

Super‐Resolution reconstruction produces one or a set of high‐resolution images from a
sequence of low‐resolution frames. This article reviews a variety of Super‐Resolution …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

Although multiframe super resolution has been extensively studied in past decades, super
resolving real-world video sequences still remains challenging. In existing systems, either …

Methods for super-resolution can be broadly classified into two families of methods:(i) The
classical multi-image super-resolution (combining images obtained at subpixel …

We propose a new high-quality and efficient single-image upscaling technique that extends
existing example-based super-resolution frameworks. In our approach we do not rely on an …

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 …

Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-
resolution image from its low-resolution observation. To regularize the solution of the …

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 …

Visual recognition research often assumes a sufficient resolution of the region of interest
(ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution …