Stochastic Models for Sparse and Piecewise-Smooth Signals

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Stochastic Models for Sparse and Piecewise-Smooth Signals
Abstract—We introduce an extended family of continuous-domain stochastic models for sparse, piecewise-smooth signals. These are specified as solutions of stochastic differential equations, or, equivalently, in terms of a suitable innovation model; the latter is analogous conceptually to the classical interpretation of a Gaussian stationary process as filtered white noise. The two specific features of our approach are 1) signal generation is driven by a random stream of Dirac impulses (Poisson noise) instead of Gaussian white noise, and 2) the class of admissible whitening operators is considerably larger than what is allowed in the conventional theory of stationary processes. We provide a complete characterization of these finite-rate-of-innovation signals within Gelfand’s framework of generalized stochastic processes. We then focus on the class of scale-invariant whitening operators which correspond to unstable systems. We show that these can be solved by introducing proper bo...
Michael Unser, Pouya Dehghani Tafti
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TSP
Authors Michael Unser, Pouya Dehghani Tafti
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