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 …

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 …

Recent advances have seen a surge of deep learning approaches for image super-
resolution. Invariably, a network, eg a deep convolutional neural network (CNN) or auto …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a
range of problems such as denoising, deblurring and super-resolution. However, the use of …

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 …

Camera shake during exposure leads to objectionable image blur and ruins many
photographs. Conventional blind deconvolution methods typically assume frequency …

Sparse representations of signals have drawn considerable interest in recent years. The
assumption that natural signals, such as images, admit a sparse decomposition over a …

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 …

We develop a framework for learning generic, expressive image priors that capture the
statistics of natural scenes and can be used for a variety of machine vision tasks. The …