The convergence of the block cyclic projection for compressed sensing based tomography (BCPCS) algorithm had been proven recently in the case of underrelaxation parameter λ∈(0,1]. In this paper, we prove its convergence with overrelaxation parameter λ∈(1,2). As a result, the convergence of the other two algorithms (BCAVCS and BDROPCS) with overrelaxation parameter λ∈(1,2) in a special case is derived. Experiments are given to demonstrate the convergence behavior of the BCPCS algorithm with different values of λ
According to the recent theory of compressed sensing, accu-rate reconstruction is possible even from...
In this paper, we analyze the convergence properties of projected non-stationary block iterative met...
Compressed sensing is an emerging approach for signal acquisition wherein theory has shown that a sm...
The block cyclic projection method in the compressed sensing framework (BCPCS) was introduced for im...
The amalgamated projection method for convex feasibility and optimization problems has recently been...
The constrained total variation minimization has been developed successfully for image reconstructio...
In this talk, we will introduce a block diagonally-relaxed orthogonal projection algorithm and a blo...
The constrained total variation minimization has been developed successfully for image reconstructio...
In this talk, we will incorporate block iterations to a diagonally-relaxed orthogonal projection alg...
In this paper, we propose a block diagonally-relaxed orthogonal projection algorithm incorporated in...
The theory of compressed sensing has recently shown that signals and images that have sparse represe...
Compressive sensing allows a signal to be sampled at sub-Nyquist rate and still get recovered exactl...
This short paper studies convergence properties, particularly asymptotic convergence, of the block-i...
Iterative reconstruction of density pixel images from measured projections in computed tomography ha...
In this paper different ways to reduce the amount of data associated with tomographic scans were exp...
According to the recent theory of compressed sensing, accu-rate reconstruction is possible even from...
In this paper, we analyze the convergence properties of projected non-stationary block iterative met...
Compressed sensing is an emerging approach for signal acquisition wherein theory has shown that a sm...
The block cyclic projection method in the compressed sensing framework (BCPCS) was introduced for im...
The amalgamated projection method for convex feasibility and optimization problems has recently been...
The constrained total variation minimization has been developed successfully for image reconstructio...
In this talk, we will introduce a block diagonally-relaxed orthogonal projection algorithm and a blo...
The constrained total variation minimization has been developed successfully for image reconstructio...
In this talk, we will incorporate block iterations to a diagonally-relaxed orthogonal projection alg...
In this paper, we propose a block diagonally-relaxed orthogonal projection algorithm incorporated in...
The theory of compressed sensing has recently shown that signals and images that have sparse represe...
Compressive sensing allows a signal to be sampled at sub-Nyquist rate and still get recovered exactl...
This short paper studies convergence properties, particularly asymptotic convergence, of the block-i...
Iterative reconstruction of density pixel images from measured projections in computed tomography ha...
In this paper different ways to reduce the amount of data associated with tomographic scans were exp...
According to the recent theory of compressed sensing, accu-rate reconstruction is possible even from...
In this paper, we analyze the convergence properties of projected non-stationary block iterative met...
Compressed sensing is an emerging approach for signal acquisition wherein theory has shown that a sm...