We propose a method for constructing a spherical harmonic sensing matrix that can be used to effectively recover a sparse signal on the sphere from limited measurements. For such a sensing matrix, in a compressed sensing setting, it is desirable that th
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
In this paper, we show how perturbation can affect the reconstruction of sparse spherical harmonic (...
In this thesis, we investigate the possibility of reducing the number of measurements and using only...
In this thesis, we investigate the possibility of reducing the number of measurements and using only...
A sampling theorem on the sphere has been developed recently, requiring half as many samples as alte...
We discuss a novel sampling theorem on the sphere developed by McEwen & Wiaux recently through an as...
It is difficult to determine whether or not the restricted isometry property (RIP) holds when measur...
Abstract—We study the impact of sampling theorems on the fidelity of sparse image reconstruction on ...
We show that sparse spherical harmonic expansions can be efficiently recovered from a small number o...
Compressed sensing is a technique for recovering an unknown sparse signal from a small number of lin...
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate pr...
Compressed Sensing concerns a new class of linear data acquisition protocols that are more efficient...
Abstract—Compressed sensing is designed to measure sparse signals directly in a compressed form. How...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
In this paper, we show how perturbation can affect the reconstruction of sparse spherical harmonic (...
In this thesis, we investigate the possibility of reducing the number of measurements and using only...
In this thesis, we investigate the possibility of reducing the number of measurements and using only...
A sampling theorem on the sphere has been developed recently, requiring half as many samples as alte...
We discuss a novel sampling theorem on the sphere developed by McEwen & Wiaux recently through an as...
It is difficult to determine whether or not the restricted isometry property (RIP) holds when measur...
Abstract—We study the impact of sampling theorems on the fidelity of sparse image reconstruction on ...
We show that sparse spherical harmonic expansions can be efficiently recovered from a small number o...
Compressed sensing is a technique for recovering an unknown sparse signal from a small number of lin...
We give a new, very general, formulation of the compressed sensing problem in terms of coordinate pr...
Compressed Sensing concerns a new class of linear data acquisition protocols that are more efficient...
Abstract—Compressed sensing is designed to measure sparse signals directly in a compressed form. How...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
A sequential adaptive compressed sensing procedure for signal support recovery is proposed and analy...
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