D
David L. Donoho
Researcher at Stanford University
Publications - 273
Citations - 115802
David L. Donoho is an academic researcher from Stanford University. The author has contributed to research in topics: Wavelet & Compressed sensing. The author has an hindex of 110, co-authored 271 publications receiving 108027 citations. Previous affiliations of David L. Donoho include University of California, Berkeley & Western Geophysical.
Papers
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Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit
TL;DR: Stagewise Orthogonal Matching Pursuit (StOMP) successively transforms the signal into a negligible residual, and numerical examples showing that StOMP rapidly and reliably finds sparse solutions in compressed sensing, decoding of error-correcting codes, and overcomplete representation are given.
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Minimax estimation via wavelet shrinkage
TL;DR: A nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients is developed, andVariants of this method based on simple threshold nonlinear estimators are nearly minimax.
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For most large underdetermined systems of equations, the minimal 1-norm near-solution approximates the sparsest near-solution
TL;DR: It is shown that for most Φ, if the optimally sparse approximation x0,ϵ is sufficiently sparse, then the solution x1, ϵ of the 𝓁1‐minimization problem is a good approximation to x0 ,ϵ.
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Image decomposition via the combination of sparse representations and a variational approach
TL;DR: A novel method for separating images into texture and piecewise smooth (cartoon) parts, exploiting both the variational and the sparsity mechanisms is presented, combining the basis pursuit denoising (BPDN) algorithm and the total-variation (TV) regularization scheme.
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Uncertainty principles and signal recovery
David L. Donoho,Philip B. Stark +1 more
TL;DR: In this article, the uncertainty principle can be generalized to cases where the sets of concentration are not intervals, and generalizations explain interesting phenomena in signal recovery problems where there is an interplay of missing data, sparsity, and bandlimiting.