From Bernoulli-Gaussian deconvolution to sparse signal restoration

Charles Soussen (1) , Jérôme Idier (2) , David Brie (1) , Junbo Duan (1)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

Formulated as a least square problem under an $\ell_0$ constraint, sparse signal restoration is a discrete optimization problem, known to be NP complete. Classical algorithms include, by increasing cost and efficiency, Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Orthogonal Least Squares (OLS), stepwise regression algorithms and the exhaustive search. We revisit the Single Most Likely Replacement (SMLR) algorithm, developed in the mid-80's for Bernoulli-Gaussian signal restoration. We show that the formulation of sparse signal restoration as a limit case of Bernoulli-Gaussian signal restoration leads to an $\ell_0$-penalized least square minimization problem, to which SMLR can be straightforwardly adapted. The resulting algorithm, called Single Best Replacement (SBR), can be interpreted as a forward-backward extension of OLS sharing similarities with stepwise regression algorithms. Some structural properties of SBR are put forward. A fast and stable implementation is proposed. The approach is illustrated on two inverse problems involving highly correlated dictionaries. We show that SBR is very competitive with popular sparse algorithms in terms of trade-off between accuracy and computation time.

Mots clés: Sparse signal estimation; inverse problems; Bernoulli-Gaussian signal restoration; SMLR algorithm; mixed $\ell_2$-$\ell_0$ criterion minimization; Orthogonal Least Squares; stepwise regression algorithms; stepwise regression algorithms.

Domaines: Automatique / Robotique

Charles Soussen, Jérôme Idier, David Brie, Junbo Duan. From Bernoulli-Gaussian deconvolution to sparse signal restoration. IEEE Transactions on Signal Processing, 2011, 59 (10), pp.4572-4584. ⟨10.1109/TSP.2011.2160633⟩. ⟨hal-00443842v4⟩