Partially linear estimation with application to image deblurring using blurred/noisy image pairs
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Pattern Recognition Letters
Copy number variation estimation from multiple next-generation sequencing samples
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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-1980s 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 tradeoff between accuracy and computation time.