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• W2936395840 startingPage "831" @default.
• W2936395840 abstract "In this paper, we consider the recovery of group sparse signals corrupted by impulsive noise. In some recent literature, researchers have utilized stable data fitting models, like l1-norm, Huber penalty function and Lorentzian-norm, to substitute the l2-norm data fidelity model to obtain more robust performance. In this paper, a stable model is developed, which exploits the generalized lp-norm as the measure for the error for sparse reconstruction. In order to address this model, we propose an efficient alternative direction method of multipliers, which includes the proximity operator of lp-norm functions to the framework of Lagrangian methods. Besides, to guarantee the convergence of the algorithm in the case of 0≤p<1 (nonconvex case), we took advantage of a smoothing strategy. For both 0≤p<1 (nonconvex case) and 1≤p≤2 (convex case), we have derived the conditions of the convergence for the proposed algorithm. Moreover, under the block restricted isometry property with constant δτk0<τ/(4−τ) for 0<τ<4/3 and δτk0<(τ−1)/τ for τ≥4/3, a sharp sufficient condition for group sparse recovery in the presence of impulsive noise and its associated error upper bound estimation are established. Numerical results based on the synthetic block sparse signals and the real-world FECG signals demonstrate the effectiveness and robustness of new algorithm in highly impulsive noise." @default.
• W2936395840 created "2019-04-25" @default.
• W2936395840 creator A5007384450 @default.
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• W2936395840 date "2020-11-01" @default.
• W2936395840 modified "2023-10-11" @default.
• W2936395840 title "Group sparse recovery in impulsive noise via alternating direction method of multipliers" @default.
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• W2936395840 doi "https://doi.org/10.1016/j.acha.2019.04.002" @default.
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