Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have
carried out two independent processes to synthesize high-resolution (HR) image patches. In …

In this paper, we propose an image compression-encryption method based on two-
dimensional (2D) sparse representation and chaotic system. In the first step of this method …

We propose a modified sequential quadratic programming method for solving the sparse
signal recovery problem. We start by going through the well-known smoothed-ℓ 0 technique …

As a classic method of nonlinear dimensional reduction, locally linear embedding (LLE) is
more and more attractive to researchers due to its ability to deal with large amounts of high …

The goal of nonlinear dimensionality reduction is to find the meaningful low dimensional
structure of the nonlinear manifold from the high dimensional data. As a classic method of …

Sparse signal restoration is usually formulated as the minimization of a quadratic cost
function| y-Ax|| 2 2 where\mbi A is a dictionary and\mbi x is an unknown sparse vector. It is …

The sparse recovery methods utilize the ℓ p-norm-based regularization in the estimation
problem with 0≤ p≤ 1. These methods have a better utility when the number of …

In this paper, we address recovery of sparse signals from compressed measurements, and
sparse signal approximation, which have received considerable attention over the last …

As an alternative to adaptive nonlinear schemes for dimensionality reduction, linear random
projection has recently proved to be a reliable means for high-dimensional data processing …

In this paper, based on a successively accuracy-increasing approximation of the ℓ 0 norm,
we propose a new algorithm for recovery of sparse vectors from underdetermined …