Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization

TL;DR

This paper introduces AtomicLift, a convex optimization method that guarantees the recovery of sparse spike signals and unknown point spread functions from limited samples, even in noisy conditions, under mild assumptions.

Contribution

It proposes a novel convex framework combining lifting and atomic norm minimization to jointly estimate spike signals and unknown point spread functions.

Findings

Guaranteed recovery of spike signals under mild conditions.

Effective in noisy measurement scenarios.

Validated with numerical experiments.

Abstract

Neural recordings, returns from radars and sonars, images in astronomy and single-molecule microscopy can be modeled as a linear superposition of a small number of scaled and delayed copies of a band-limited or diffraction-limited point spread function, which is either determined by the nature or designed by the users; in other words, we observe the convolution between a point spread function and a sparse spike signal with unknown amplitudes and delays. While it is of great interest to accurately resolve the spike signal from as few samples as possible, however, when the point spread function is not known a priori, this problem is terribly ill-posed. This paper proposes a convex optimization framework to simultaneously estimate the point spread function as well as the spike signal, by mildly constraining the point spread function to lie in a known low-dimensional subspace. By applying the lifting trick, we obtain an underdetermined linear system of an ensemble of signals with joint spectral sparsity, to which atomic norm minimization is applied. Under mild randomness assumptions of the low-dimensional subspace as well as a separation condition of the spike signal, we prove the proposed algorithm, dubbed as AtomicLift, is guaranteed to recover the spike signal up to a scaling factor as soon as the number of samples is large enough. The extension of AtomicLift to handle noisy measurements is also discussed. Numerical examples are provided to validate the effectiveness of the proposed approaches.