Multichannel Sparse Blind Deconvolution on the Sphere

Yanjun Li, Yoram Bresler

Research output: Contribution to journalArticlepeer-review

Abstract

Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_{i}$ from their circular convolution $y_{i}=x_{i} \circledast f$ ( $i=1,2, {\dots },N$ ). We consider the case where the $x_{i}$ 's are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a filter $h$ on the unit sphere that produces sparse output $y_{i}\circledast h$. Under some technical assumptions, we show that all local minima of the objective function correspond to the inverse filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_{i}$ using a simple manifold gradient descent (MGD) algorithm. The same approach is also applicable to blind gain and phase calibration with a Fourier sensing matrix. Our algorithm and analysis require fewer assumptions than previous algorithms for the same problem. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods. Empirically, our algorithm has low computation cost (converging in a small number of iterations) and low memory footprint (solving only for the inverse filter of $f$ ).

Original languageEnglish (US)
Article number8762219
Pages (from-to)7415-7436
Number of pages22
JournalIEEE Transactions on Information Theory
Volume65
Issue number11
DOIs
StatePublished - Nov 2019

Keywords

  • Manifold gradient descent
  • Riemannian Hessian
  • Riemannian gradient
  • nonconvex optimization
  • strict saddle points
  • super-resolution fluorescence microscopy

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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