文件名称:Matlab_code_Q-VMP
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Compressive sensing is the reconstruction of sparse images
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
-Compressive sensing is the reconstruction of sparse images
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
-Compressive sensing is the reconstruction of sparse images
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
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下载文件列表
Matlab_code_Q-VMP\COPYING.txt
.................\License.txt
.................\Quantizer.m
.................\Q_VMP.m
.................\README.asv
.................\README.txt
.................\test_Q_VMP_1bit.m
.................\test_Q_VMP_multibit.m
Matlab_code_Q-VMP