Publications related to Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Sparse inverse problems for Fourier imaging

Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain. The recent theory of Compressed Sensing exploits this property offering a powerful frame ...

EPFL2017

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

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

In this paper, the problem of speech source localization and separation from recordings of convolutive underdetermined mixtures is studied. The problem is cast as recovering the spatio-spectral speech information embedded in a microphone array compressed m ...

Elsevier Science Bv2016

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Michaël Unser, Masih Nilchian, Michael Thompson McCann

We present a 3D iterative reconstruction method for microtomography, where iterative methods have previously been limited by their high computational cost. Our preliminary results show that it improves reconstruction quality over the traditional slice-by-s ...

OSA2016

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Masih Nilchian, Marco Stampanoni

Reconstruction of underconstrained tomographic data sets remains a major challenge. Standard analytical techniques frequently lead to unsatisfactory results due to insufficient information. Several iterative algorithms, which can easily integrate a priori ...

IEEE2016

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Martin Kurt Storath

The Mumford-Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this pap ...

Iop Publishing Ltd2015

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Volkan Cevher

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Total Variation Regularization of Shape Signals

Martin Kurt Storath

This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-va ...

IEEE2015

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

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

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Sparse inverse problems for Fourier imaging

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Total Variation Regularization of Shape Signals

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

Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Sparse inverse problems for Fourier imaging

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

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

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

Total Variation Regularization of Shape Signals

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost