Publications associées à PURIFY: a new approach to radio-interferometric imaging

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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

PURIFY: a new approach to radio-interferometric imaging

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Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Global Optimization for Image Restoration in Optical/IR Interferometry

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Mumford-Shah and Potts Regularization for Manifold-Valued Data

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

A compressed beamforming framework for ultrafast ultrasound imaging

Robust sparse image reconstruction of radio interferometric observations with PURIFY

On the influence of momentum acceleration on online learning

Morphological component analysis for sparse regularization in plane wave imaging

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

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Global Optimization for Image Restoration in Optical/IR Interferometry

Robust sparse image reconstruction of radio interferometric observations with PURIFY

Morphological component analysis for sparse regularization in plane wave imaging

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

A compressed beamforming framework for ultrafast ultrasound imaging

On the influence of momentum acceleration on online learning