Publications associées à Variational Approach to Tomographic Reconstruction

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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

Variational Approach to Tomographic Reconstruction

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

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

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

Proximity Operators for Phase Retrieval

Structure Modeling of High Dimensional Data

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Structure Tensor Total Variation

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Sensing the real world

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

Proximity Operators for Phase Retrieval

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

Structure Tensor Total Variation

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

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

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Structure Modeling of High Dimensional Data

Sensing the real world

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds