430 citations · 1.6k across the 21 of their papers we have counts for
23 papers
Bregman Plug-and-Play Priors
Abdullah H. Al-Shabili, Xiaojian Xu, Ivan Selesnick +1
The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play…
Epigraphical Relaxation for Minimizing Layered Mixed Norms
Seisuke Kyochi, Shunsuke Ono, Ivan Selesnick
This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed norm minimization. Mixed norm regularization methods play a central role in signal reconstru…
Image Fusion via Sparse Regularization with Non-Convex Penalties
Nantheera Anantrasirichai, Rencheng Zheng, Ivan Selesnick +1
The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) perf…
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…
On non-optimal spectral factorizations
L. Ephremidze, I. Selesnick, I. Spitkovsky
For a given Laurent polynomial matrix function , which is positive definite on the unit circle in the complex plane, we consider all possible polynomial spectral factors of …
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…
Repetitive Transients Extraction Algorithm for Detecting Bearing Faults
Wangpeng He, Yin Ding, Yanyang Zi +1
This paper addresses the problem of noise reduction with simultaneous components extrac- tion in vibration signals for faults diagnosis of bearing. The observed vibration signal is…
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…
Enhanced Low-Rank Matrix Approximation
Ankit Parekh, Ivan W. Selesnick
This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with non-convex regularization. We employ parameterized non-convex penalty functions…
Detection of Faults in Rotating Machinery Using Periodic Time-Frequency Sparsity
Yin Ding, Wangpeng He, Binqiang Chen +2
This paper addresses the problem of extracting periodic oscillatory features in vibration sig- nals for detecting faults in rotating machinery. To extract the feature, we propose a…
Sparsity-based Algorithm for Detecting Faults in Rotating Machines
Wangpeng He, Yin Ding, Yanyang Zi +1
This paper addresses the detection of periodic transients in vibration signals for detecting faults in rotating machines. For this purpose, we present a method to estimate periodic…
Sparsity-based Correction of Exponential Artifacts
Yin Ding, Ivan W. Selesnick
This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some me…
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…
Convex Denoising using Non-Convex Tight Frame Regularization
Ankit Parekh, Ivan W. Selesnick
This paper considers the problem of signal denoising using a sparse tight-frame analysis prior. The L1 norm has been extensively used as a regularizer to promote sparsity; however,…
Image Restoration using Total Variation with Overlapping Group Sparsity
Jun Liu, Ting-Zhu Huang, Ivan W. Selesnick +2
Image restoration is one of the most fundamental issues in imaging science. Total variation (TV) regularization is widely used in image restoration problems for its capability to p…
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Po-Yu Chen, Ivan W. Selesnick
Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than con…
Translation-Invariant Shrinkage/Thresholding of Group Sparse Signals
Po-Yu Chen, Ivan W. Selesnick
This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture the tendency of coeff…
Sparse Frequency Analysis with Sparse-Derivative Instantaneous Amplitude and Phase Functions
Yin Ding, Ivan W. Selesnick
This paper addresses the problem of expressing a signal as a sum of frequency components (sinusoids) wherein each sinusoid may exhibit abrupt changes in its amplitude and/or phase.…
Sparse Signal Estimation by Maximally Sparse Convex Optimization
Ivan W. Selesnick, Ilker Bayram
This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g. sparse deconvolution. This paper aims to induce sparsity mor…
Compressed Sensing for Moving Imagery in Medical Imaging
Cagdas Bilen, Yao Wang, Ivan Selesnick
Numerous applications in signal processing have benefited from the theory of compressed sensing which shows that it is possible to reconstruct signals sampled below the Nyquist rat…
High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing
Cagdas Bilen, Yao Wang, Ivan Selesnick
Magnetic Resonance Imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher…