Identifiability and Stability in Blind Deconvolution under Minimal Assumptions

TL;DR

This paper establishes optimal sample complexity bounds for blind deconvolution under minimal assumptions, demonstrating conditions for unique and stable recovery with high probability.

Contribution

It derives the first optimal sample complexity results for blind deconvolution with generic bases or frames, improving upon previous suboptimal bounds.

Findings

Optimal sample complexity of n > s_1 + s_2 for sparse vectors

Stable recovery with high probability under probabilistic models

Extension of results to subspace and mixed constraints

Abstract

Blind deconvolution (BD) arises in many applications. Without assumptions on the signal and the filter, BD does not admit a unique solution. In practice, subspace or sparsity assumptions have shown the ability to reduce the search space and yield the unique solution. However, existing theoretical analysis on uniqueness in BD is rather limited. In an earlier paper, we provided the first algebraic sample complexities for BD that hold for almost all bases or frames. We showed that for BD of a pair of vectors in $\mathbb{C}^n$, with subspace constraints of dimensions $m_1$ and $m_2$, respectively, a sample complexity of $n\geq m_1m_2$ is sufficient. This result is suboptimal, since the number of degrees of freedom is merely $m_1+m_2-1$. We provided analogus results, with similar suboptimality, for BD with sparsity or mixed subspace and sparsity constraints. In this paper, taking advantage of the recent progress on the information-theoretic limits of unique low-rank matrix recovery, we finally bridge this gap, and derive an optimal sample complexity result for BD with generic bases or frames. We show that for BD of an arbitrary pair (resp. all pairs) of vectors in $\mathbb{C}^n$, with sparsity constraints of sparsity levels $s_1$ and $s_2$, a sample complexity of $n > s_1+s_2$ (resp. $n > 2(s_1+s_2)$) is sufficient. We also present analogous results for BD with subspace constraints or mixed constraints, with the subspace dimension replacing the sparsity level. Last but not least, in all the above scenarios, if the bases or frames follow a probabilistic distribution specified in the paper, the recovery is not only unique, but also stable against small perturbations in the measurements, under the same sample complexities.