Package smile.cs
package smile.cs
Compressed sensing is a signal processing technique for efficiently
acquiring and reconstructing a signal by finding solutions to
underdetermined linear systems. This is based on the principle that,
through optimization, the sparsity of a signal can be exploited to
recover it from far fewer samples than required by the Nyquist–Shannon
sampling theorem. There are two conditions under which recovery is possible.
The first one is sparsity, which requires the signal to be sparse
in some domain. The second one is incoherence, which is applied through
the isometric property, which is sufficient for sparse signals.
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Record ClassesClassDescriptionBasis Pursuit Denoising (BPDN) via a log-barrier interior-point method.Hyperparameters for the log-barrier interior-point solver.Compressive Sampling Matching Pursuit (CoSaMP) for sparse signal recovery.Hyperparameters for CoSaMP.Measurement matrix for compressed sensing.Orthogonal Matching Pursuit (OMP) for sparse signal recovery.Hyperparameters for OMP.