Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees

DOI10.1002/cpa.21805zbMath1459.94029arXiv1707.00808OpenAlexW2962851924WikidataQ128873008 ScholiaQ128873008MaRDI QIDQ5381058

Carlos Fernandez-Granda, Brett Bernstein

Publication date: 7 June 2019

Published in: Communications on Pure and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.00808

zbMATH Keywords

convex optimizationsampling theorydeconvolutionimpulsive noisesparsitysuper-resolutiondual certificateGaussian convolutionnonuniform samplingRicker wavelet

Mathematics Subject Classification ID

Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Convex programming (90C25) Applications of mathematical programming (90C90) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)

Related Items (3)

Sparse non-negative super-resolution -- simplified and stabilised ⋮ A mathematical theory of the computational resolution limit in one dimension ⋮ A Sampling Theorem for Deconvolution in Two Dimensions

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