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- W2727747412 abstract "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 $ell_1$ 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." @default.
- W2727747412 created "2017-07-14" @default.
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- W2727747412 date "2017-07-04" @default.
- W2727747412 modified "2023-09-23" @default.
- W2727747412 title "Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees" @default.
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