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Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees
Author(s) -
Bernstein Brett,
FernandezGranda Carlos
Publication year - 2019
Publication title -
communications on pure and applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.12
H-Index - 115
eISSN - 1097-0312
pISSN - 0010-3640
DOI - 10.1002/cpa.21805
Subject(s) - deconvolution , mathematics , robustness (evolution) , wavelet , gaussian , kernel (algebra) , mathematical optimization , algorithm , regular polygon , blind deconvolution , computer science , discrete mathematics , artificial intelligence , biochemistry , chemistry , physics , geometry , quantum mechanics , gene
In this work we analyze a convex‐programming method for estimating superpositions of point sources or spikes from nonuniform samples of their convolution with a known kernel. We consider a one‐dimensional model where the kernel is either a Gaussian function or a Ricker wavelet, inspired by applications in geophysics and imaging. Our analysis establishes that minimizing a continuous counterpart of the ℓ 1 ‐norm achieves exact recovery of the original spikes as long as (1) the signal support satisfies a minimum‐separation condition and (2) there are at least two samples close to every spike. In addition, we derive theoretical guarantees on the robustness of the approach to both dense and sparse additive noise. © 2018 Wiley Periodicals, Inc.