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 …

Recently, position-patch based approaches have been proposed to replace the probabilistic
graph-based or manifold learning-based models for face hallucination. In order to obtain the …

Restoration of fog images is important for the deweathering issue in computer vision. The
problem is ill-posed and can be regularized within a Bayesian context using a probabilistic …

Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are
locally isometric, the neighbor embedding super-resolution algorithms try to preserve the …

In human-machine interaction, human face is one of the core factors. However, due to the
limitations of imaging conditions and low-cost imaging sensors, the captured faces are often …

Recently, locality-constrained linear coding (LLC) has been drawn great attentions and
been widely used in image processing and computer vision tasks. However, the …

Most face super-resolution methods assume that low-and high-resolution manifolds have
similar local geometrical structure; hence, learn local models on the low-resolution manifold …

A popular approach for single image super-resolution (SR) is to use scaled down versions of
the given image to build an internal training dictionary of pairs of low resolution (LR) and …

This paper presents a novel approach for the reconstruction of super-resolution (SR)
synthetic aperture radar (SAR) images in the compressed sensing (CS) theory framework …

Super-resolution includes the techniques which deal with the methods of converting the low-
resolution image into the high-resolution image. In this paper, various challenges affecting …