SAMV (algorithm)

SAMViterative Sparse Asymptotic Minimum Variance
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.wikipedia
36 Related Articles

Super-resolution imaging

super-resolutionsuperresolutionsuper resolution
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing. MUltiple SIgnal Classification (MUSIC), a popular parametric superresolution method
In some radar and sonar imaging applications (e.g., magnetic resonance imaging (MRI), high-resolution computed tomography), subspace decomposition-based methods (e.g., MUSIC ) and compressed sensing-based algorithms (e.g., SAMV ) are employed to achieve SR over standard periodogram algorithm.

Inverse problem

inverse problemsinversioninverse method
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.
Solutions explored include Algebraic Reconstruction Technique, filtered backprojection, and as computing power has increased, iterative reconstruction methods such as iterative Sparse Asymptotic Minimum Variance.

Synthetic-aperture radar

synthetic aperture radarSARsynthetic aperture
Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI).
SAMV method is a parameter-free sparse signal reconstruction based algorithm.

Tomographic reconstruction

reconstructedreconstruction algorithmtomography
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.
An alternative family of recursive tomographic reconstruction algorithms are the Algebraic Reconstruction Technique s and iterative Sparse Asymptotic Minimum Variance.

Direction of arrival

direction-of-arrival
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.
SAMV

Magnetic resonance imaging

MRImagnetic resonance imaging (MRI)magnetic resonance
Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI).
However, recent compressed sensing-based software algorithms (e.g., SAMV ) have been proposed to achieve super-resolution without requiring such high field strengths.

Sensor array

uniform linear array
Suppose an M-element uniform linear array (ULA) receive K narrow band signals emitted from sources located at locations, respectively.
SAMV beamforming algorithm is a sparse signal reconstruction based algorithm which explicitly exploits the time invariant statistical characteristic of the covariance matrix.

CT scan

computed tomographyCTcomputerized tomography
Applications include synthetic-aperture radar, computed tomography scan, and magnetic resonance imaging (MRI).
1) New software technology can significantly reduce the required radiation dose. New iterative tomographic reconstruction algorithms (e.g., iterative Sparse Asymptotic Minimum Variance) could offer superresolution without requiring higher radiation dose.

Radon transform

filtered back projectionfiltered backprojectionsinograms
This imaging problem is a single-snapshot application, and algorithms compatible with single-snapshot estimation are included, i.e., Matched filter (MF, similar to the periodogram or backprojection, which is often efficiently implemented as fast Fourier transform (FFT)), IAA, and a variant of the SAMV algorithm (SAMV-0).
Iterative reconstruction methods (e.g., iterative Sparse Asymptotic Minimum Variance ) could provide metal artifact reduction, noise and dose reduction for the reconstructed result that attract much research interest around the world.

Matched filter

matched filteringmatched-filteringNorth filters
This imaging problem is a single-snapshot application, and algorithms compatible with single-snapshot estimation are included, i.e., Matched filter (MF, similar to the periodogram or backprojection, which is often efficiently implemented as fast Fourier transform (FFT)), IAA, and a variant of the SAMV algorithm (SAMV-0).
SAMV

Periodogram

This imaging problem is a single-snapshot application, and algorithms compatible with single-snapshot estimation are included, i.e., Matched filter (MF, similar to the periodogram or backprojection, which is often efficiently implemented as fast Fourier transform (FFT)), IAA, and a variant of the SAMV algorithm (SAMV-0).
SAMV

MUSIC (algorithm)

MUltiple SIgnal ClassificationMUSICMUSIC (Multiple Signal Classification)
MUltiple SIgnal Classification (MUSIC), a popular parametric superresolution method
Recent iterative semi-parametric methods offer robust superresolution despite of highly correlated sources, e.g., SAMV

Spectral density estimation

spectral estimationfrequency estimationspectral analysis
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.

Signal processing

signal analysissignalsignal processor
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.

Medical imaging

imagingdiagnostic imagingdiagnostic radiology
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.

Remote sensing

remote-sensingremotely sensedremote sensor
SAMV (iterative Sparse Asymptotic Minimum Variance ) is a parameter-free superresolution algorithm for the linear inverse problem in spectral estimation, direction-of-arrival (DOA) estimation and tomographic reconstruction with applications in signal processing, medical imaging and remote sensing.

Correlation coefficient

correlationcorrelatedcorrelation coefficients
It is a powerful tool for the recovery of both the amplitude and frequency characteristics of multiple highly correlated sources in challenging environment (e.g., limited number of snapshots, low signal-to-noise ratio.

Signal-to-noise ratio

signal to noise ratioSNRsignal-to-noise
It is a powerful tool for the recovery of both the amplitude and frequency characteristics of multiple highly correlated sources in challenging environment (e.g., limited number of snapshots, low signal-to-noise ratio.

Phased array

phased array radarphased-arrayphased-array radar
where is the steering matrix, contains the source waveforms, and {\bf e}(n) is the noise term.

Dirac delta function

delta functionimpulsedirac delta
Assume that, where is the Dirac delta and it equals to 1 only if n=\bar{n} and 0 otherwise.

Covariance matrix

variance-covariance matrixcovariance matricescovariance
The covariance matrix of {\bf y}(n) that contains all information about is

Vectorization (mathematics)

vectorizationvectorization operatorhalf-vectorization
After applying the vectorization operator to the matrix {\bf R}, the obtained vector is linearly related to the unknown parameter as

Compressed sensing

compressive sensingcompressed sensing techniquesCompressed-Sensing
The resolution of most compressed sensing based source localization techniques is limited by the fineness of the direction grid that covers the location parameter space.

Sparse dictionary learning

dictionary learningdictionary matrixMini-batch dictionary learning
In the sparse signal recovery model, the sparsity of the truth signal is dependent on the distance between the adjacent element in the overcomplete dictionary {\bf A}, therefore, the difficulty of choosing the optimum overcomplete dictionary arises.