Publications related to Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of the samples is known and that the only distortion present in the observations is ...

Ieee2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Structure Tensor Total Variation

Michaël Unser, Stamatios Lefkimmiatis

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

Michaël Unser, Nikolaos Stergiopoulos, Emrah Bostan, Orestis Vardoulis, Stamatios Lefkimmiatis

We propose a new variational framework for the problem of reconstructing flow fields from noisy measurements. The formalism is based on regularizers penalizing the singular values of the Jacobian of the field. Specifically, we rely on the nuclear norm. Our ...

Ieee-Inst Electrical Electronics Engineers Inc2015

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Rolf Gruetter, Yves Wiaux, José Pedro Rebelo Ferreira Marques, Diana Khabipova

The aim of this study is to perform a thorough comparison of quantitative susceptibility mapping (QSM) techniques and their dependence on the assumptions made. The compared methodologies were: two iterative single orientation methodologies minimizing the l ...

Elsevier2015

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools for the optimization of robotic controllers, which have inherently noisy performance evaluations. This article discusses how the result ...

2014

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

Joachim Krieger

For the radial energy-supercritical nonlinear wave equation

\Box u = -u_{tt} + \triangle u = \pm u^7

on

\R^{3+1}

, we prove the existence of a class of global in forward time

C^\infty

-smooth solutions with infinite critical Sobolev norm $\dot{H}^{\f ...

Springer Verlag2014

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Inverse problems with shot noise arise in many modern biomedical imaging applications. The main challenge is to obtain an estimate of the underlying specimen from measurements corrupted by Poisson noise. In this work, we propose an efficient framework for ...

IEEE2013

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

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Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Structure Tensor Total Variation

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Acoustic DoA Estimation by One Unsophisticated Sensor

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Structure Tensor Total Variation

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data