Publications associées à Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

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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

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

Total Variation Regularization of Shape Signals

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

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

Sparse inverse problems for Fourier imaging

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

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

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

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

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