The choice of the over-complete dictionary that sparsely represents data is of prime
importance for sparse coding-based image super-resolution. Sparse coding is a typical …

Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse
coding is a typical unsupervised learning method to study the relationship between the …

Recently, single image super-resolution reconstruction (SISR) via sparse coding has
attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries …

Recent years have shown a growing research interest in the sparse-representation of
signals. Signals are described through sparse linear combinations of signal-atoms over a …

Based on the assumption about the sparse representation of natural images and the theory
of compressed sensing, very promising results about single-image super-resolution were …

We present a computationally efficient architecture for image super-resolution that achieves
state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional …

Super resolution is of great use in many visual media related scenarios, such as displaying
low resolution contents on High-Definition TV (HD-TV). In these scenarios, the efficiency of …

Sparse coding (SC) plays an important role in versatile computer vision applications such as
image super-resolution (SR). Most of the previous SC based SR methods partition the image …

In this paper, we present a unified frame based on collaborative representation (CR) for
single-image super-resolution (SR), which learns low-resolution (LR) and high-resolution …

In this paper, we present a new learning-based single-image super-resolution (SR)
approach, inspired by existing sparse representation-based methods. As a promising image …