On Asymptotic Incoherence and Its Implications for Compressed Sensing of Inverse Problems

Recently, it has been shown that incoherence is an unrealistic assumption for compressed sensing when applied to many inverse problems. Instead, the key property that permits efficient recovery in such problems is the so-called local incoherence. Similarly, the standard notion of sparsity is also inadequate for many real-world problems. In particular, in many applications, the optimal sampling strategy depends on asymptotic incoherence and the signal sparsity structure. The purpose of this paper is to study asymptotic incoherence and its implications toward the design of optimal sampling strategies and efficient sparsity bases. How fast asymptotic incoherence can decay in general for isometries is determined. Furthermore, it is shown that Fourier sampling and wavelet sparsity, while globally coherent, yield optimal asymptotic incoherence as a power law up to a constant factor. Sharp bounds on the asymptotic incoherence for Fourier sampling with polynomial bases are also provided. A numerical experiment is also presented to demonstrate the role of asymptotic incoherence in finding good subsampling strategies.