The performance of traditional face recognition systems is sharply reduced when
encountered with a low-resolution (LR) probe face image. To obtain much more detailed …

Motivated by the fact that image patches could be inherently represented by matrices, single-
image super-resolution is treated as a problem of learning regression operators in a matrix …

Many existed example-based super-resolution algorithms focus on learning a regression
function which maps a low-resolution image patch to a high-resolution image patch. Even …

Sparse representation and dictionary learning of image patches are well-known methods for
single-image super-resolution. However, due to the regularization term of sparse-inducing …

This paper proposes a novel registration and super-resolution jointed paradigm for medical
images under the Internet of thing environment. In the medical image processing, the …

The coefficient of high resolution (HR) block and low resolution (LR) block is assumed to be
equal in selecting the corresponding atoms of HR dictionary in previous super-resolution …