430 citations · 1.6k across the 21 of their papers we have counts for
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Sparse Regularization via Convex Analysis
Ivan Selesnick
Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alter…
Total Variation Denoising via the Moreau Envelope
Ivan Selesnick
Total variation denoising is a nonlinear filtering method well suited for the estimation of piecewise-constant signals observed in additive white Gaussian noise. The method is defi…
Improved Sparse Low-Rank Matrix Estimation
Ankit Parekh, Ivan W. Selesnick
We address the problem of estimating a sparse low-rank matrix from its noisy observation. We propose an objective function consisting of a data-fidelity term and two parameterized…
The Douglas-Rachford Algorithm for Weakly Convex Penalties
İlker Bayram, Ivan W. Selesnick
The Douglas-Rachford algorithm is widely used in sparse signal processing for minimizing a sum of two convex functions. In this paper, we consider the case where one of the functio…
Enhanced Sparsity by Non-Separable Regularization
Ivan W. Selesnick, Iker Bayram
This paper develops a convex approach for sparse one-dimensional deconvolution that improves upon L1-norm regularization, the standard convex approach. We propose a sparsity-induci…
Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection
Ankit Parekh, Ivan W. Selesnick
We propose a convex formulation of the fused lasso signal approximation problem consisting of non-convex penalty functions. The fused lasso signal model aims to estimate a sparse p…