Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees
Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees
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 …