Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees
Provides theoretical guarantees for a convex deconvolution method, addressing a fundamental problem in geophysics and imaging with practical sampling constraints.
This work proves that minimizing a continuous ℓ1 norm exactly recovers superpositions of point sources from nonuniform samples of their convolution with a Gaussian or Ricker wavelet, under a minimum-separation condition and at least two samples per spike, with robustness guarantees to additive noise.
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.