Publications related to Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Learning Proximal Operators with Gaussian Processes

Colin Neil Jones, Georgios Stathopoulos, Xuan Truong Nghiem

Several distributed-optimization setups involve a group of agents coordinated by a central entity (coordinator), altogether operating in a collaborative framework. In such environments, it is often common that the agents solve proximal minimization problem ...

IEEE2018

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

Volkan Cevher, Quoc Tran Dinh

We propose two new alternating direction methods to solve “fully” nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap ...

Springer2018

Optimized Wavelet Denoising for Self-Similar alpha-Stable Processes

Michaël Unser, Pedram Pad

We investigate the performance of wavelet shrinkage methods for the denoising of symmetric-a-stable (S alpha S) self-similar stochastic processes corrupted by additive white Gaussian noise (AWGN), where a is tied to the sparsity of the process. The wavelet ...

2017

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Nicolas Henri Bernard Flammarion, Aymeric Daphnis Kévin Dieuleveut

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2017

A Functional Framework for Enhanced Ultrasound Imaging

Ultrasound systems are cheap, portable, and fast, which have become impressively popular over the last decades. State-of art imaging is however known to be sub-optimal. Most attempts to improve it formulate the problem on a discrete spatial grid and suffer ...

2017

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

Michaël Unser, Emrah Bostan, Pedram Pad, Pouria Tohidi

We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is ...

IEEE2016

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

200 km Fiber-Loop Conventional Brillouin Distributed Sensor with 2m Spatial Resolution Using Image Denoising

Luc Thévenaz, Marcelo Alfonso Soto Hernandez

Non-local means image denoising method is optimized to boost the performance of Brillouin distributed fiber sensors. Results demonstrate ultra-long sensing over a 200-km fiber-loop with 2m resolution using a sensing scheme without any hardware sophisticati ...

OSA2016

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

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Learning Proximal Operators with Gaussian Processes

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

Optimized Wavelet Denoising for Self-Similar alpha-Stable Processes

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A Functional Framework for Enhanced Ultrasound Imaging

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

Scampi: a robust approximate message-passing framework for compressive imaging

200 km Fiber-Loop Conventional Brillouin Distributed Sensor with 2m Spatial Resolution Using Image Denoising

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

200 km Fiber-Loop Conventional Brillouin Distributed Sensor with 2m Spatial Resolution Using Image Denoising

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Estimation Error of the Constrained Lasso

Learning Proximal Operators with Gaussian Processes

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A Functional Framework for Enhanced Ultrasound Imaging

Scampi: a robust approximate message-passing framework for compressive imaging

Optimized Wavelet Denoising for Self-Similar alpha-Stable Processes