DCS-AMP is a Bayesian message passing algorithm that solves the dynamic compressed sensing (DCS) problem, in which a time-varying vector of noisy measurements, y(t), is acquired from a time-varying sparse signal vector, x(t), through the linear measurement process y(t) = A(t)x(t) + e(t), where A(t) is a measurement matrix (typically with more columns than rows), and e(t) is corrupting noise. The unique feature of the DCS problem is the assumption that the support (i.e., the locations of the non-zero entries) of x(t) varies slowly over time.

DCS-AMP has been implemented in MATLAB, and has been shown to work extremely quickly, requiring only simple matrix-vector products to perform its computations. Click here to try it out.

DCS-AMP: An algorithm for efficient high-dimensional inference in the dynamic compressed sensing (DCS) problem


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