Cosparse analysis model (CAM) provides a new signal processing paradigm for recovering cosparse signals with respect to given operator from the undersampled linear measurements in context of emerging theory compressed sensing (CS). The sparse recovery/cosparse recovery is key one brought up by this paradigm. In paper, we propose family pursuit algorithms problem when obey model, termed as itera...

Abstract Compressed sensing has captured considerable attention of researchers in the past decades. In this paper, with aid powerful null space property, some deterministic recovery conditions are established for previous $$\ell _{1}$$ ℓ 1 – method and _{2}$$ 2<...

At present, the sparse recovery problem is mainly solved by convx optimization algorithm and greedy tracking method. However, former has defects in efficiency latter ability, neither of them can obtain effective under large sparsity or small observation degree. In this paper, we propose a new based on arithmetic combine ideas The proposed uses to solve coefficient signal transform domain, so as...

We formulate sparse support recovery as a salient set identification problem and use informationtheoretic analyses to characterize the recovery performance and sample complexity. We consider a very general model where we are not restricted to linear models or specific distributions. We state nonasymptotic bounds on recovery probability and a tight mutual information formula for sample complexit...