  The performance of existing approaches to the recovery of frequency-sparse
signals from compressed measurements is limited by the coherence of required
sparsity dictionaries and the discretization of frequency parameter space. In
this paper, we adopt a parametric joint recovery-estimation method based on
model selection in spectral compressive sensing. Numerical experiments show
that our approach outperforms most state-of-the-art spectral CS recovery
approaches in fidelity, tolerance to noise and computation efficiency.
