Orthogonal Matching Pursuit (OMP) algorithm is widely applied to compressive sensing (CS) image signal recovery because of its low computation complexity and its ease of implementation. However, OMP usually needs more measurements than some other recovery algorithms in order to achieve equal-quality reconstructions. This article firstly illustrates the fundamental idea of OMP and the specific algorithm steps. And then, two limitations leading to the previous issue are addressed. Finally, a sorted random matrix is proposed to be used as a measurement matrix to improve those two limitations. The experimental results show the proposed measurement matrix is able to help OMP make a great progress on the quality of recovered approximations. © 201...
Orthogonal matching pursuit (OMP) is a powerful greedy al-gorithm in compressed sensing for recoveri...
Reconstruction of sparse signals acquired in reduced dimensions requires the solution with minimum ℓ...
Conventional sensing techniques often acquire the signals entirely using a lot of resources and then...
Orthogonal Matching Pursuit (OMP) algorithm is widely applied to compressive sensing (CS) image sign...
Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery algorithms in c...
It is known that use of a random measurement (sensing) matrix usually results in good recovery perfo...
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be re...
The theory and applications on Compressed Sensing is a promising, quickly developing area which garn...
The philosophy of Compressed Sensing is that it is possible to recover a sparse signal x0 ∈ Rd from ...
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional s...
Compressive sensing(CS) is an emerging research field that has applications in signal processing, er...
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional s...
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruc...
Compressive sensing has opened up a new path to reconstruct images from a number of samples which is...
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparse...
Orthogonal matching pursuit (OMP) is a powerful greedy al-gorithm in compressed sensing for recoveri...
Reconstruction of sparse signals acquired in reduced dimensions requires the solution with minimum ℓ...
Conventional sensing techniques often acquire the signals entirely using a lot of resources and then...
Orthogonal Matching Pursuit (OMP) algorithm is widely applied to compressive sensing (CS) image sign...
Orthogonal Matching Pursuit (OMP) and Basis Pursuit (BP) are two well-known recovery algorithms in c...
It is known that use of a random measurement (sensing) matrix usually results in good recovery perfo...
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be re...
The theory and applications on Compressed Sensing is a promising, quickly developing area which garn...
The philosophy of Compressed Sensing is that it is possible to recover a sparse signal x0 ∈ Rd from ...
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional s...
Compressive sensing(CS) is an emerging research field that has applications in signal processing, er...
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional s...
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruc...
Compressive sensing has opened up a new path to reconstruct images from a number of samples which is...
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparse...
Orthogonal matching pursuit (OMP) is a powerful greedy al-gorithm in compressed sensing for recoveri...
Reconstruction of sparse signals acquired in reduced dimensions requires the solution with minimum ℓ...
Conventional sensing techniques often acquire the signals entirely using a lot of resources and then...