A Spectral Gradient Projection Method for Sparse Signal Reconstruction in Compressive Sensing

Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing

Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi g...

Compressive Sensing Reconstruction for Sparse 2D Data

In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate si...

Compressive Sensing of Sparse Signals in the Hermite Transform Basis: Analysis and Algorithm for Signal Reconstruction

—An analysis of the influence of missing samples in signals exhibiting sparsity in the Hermite transform domain is provided. Based on the statistical properties derived for the Hermite coefficients of randomly undersampled signal, the probability of success in detection of signal components support is determined. Based on the probabilistic analysis, a threshold for the detection of signal compo...

Nomonotone Spectral Gradient Method for Sparse Recovery

In the paper, we present an algorithm framework for the more general problem of minimizing the sum f(x) + ψ(x), where f is smooth and ψ is convex, but possible nonsmooth. At each step, the search direction of the algorithm is obtained by solving an optimization problem involving a quadratic term with diagonal Hessian and Barzilai-Borwein steplength plus ψ(x). The method with the nomonotone line...

Spatial Compressive Sensing for Strain Data Reconstruction from Sparse Sensors