Abstract
This paper investigates the problem of stable signal estimation from undersampled, noisy sub-Gaussian measurements under the assumption of a cosparse model. Based on generalized notions of sparsity, a novel recovery guarantee for the ℓ1-analysis basis pursuit is derived, enabling accurate predictions of its sample complexity. The bounds on the number
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