Publications related to Sensing and Recovery under Sparsity Constraints

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We consider the denoising of signals and images using regularized least-squares method. In particular, we propose a simple minimization algorithm for regularizers that are functions of the discrete gradient. By exploiting the connection of the discrete gr ...

IEEE2012

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Jean-Charles Baritaux

The recent advances in fluorescent molecular probes, photon detection instrumentation, and photon propagation models in tissue, have facilitated the emergence of innovative molecular imaging technologies such as Fluorescence Diffuse Optical Tomography (FDO ...

EPFL2012

A Dual Algorithm For L1-Regularized Reconstruction Of Vector Fields

Michaël Unser, Emrah Bostan, Pouya Dehghani Tafti

Recent advances in vector-field imaging have brought to the forefront the need for efficient denoising and reconstruction algorithms that take the physical properties of vector fields into account and can be applied to large volumes of data. With these req ...

Ieee2012

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Anastasios Kyrillidis

For linear models, compressed sensing theory and methods enable recovery of sparse signals of interest from few measurements-in the order of the number of nonzero entries as opposed to the length of the signal of interest. Results of similar flavor have mo ...

Institute of Electrical and Electronics Engineers2012

Light Field Compressive Sensing In Camera Arrays

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary t ...

Ieee2012

Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsi ...

Wiley-Blackwell2012

Light Field Compressive Sensing in Camera Arrays

Pierre Vandergheynst, Mohammad Golbabaee, Mahdad Hosseini Kamal

This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary t ...

Ieee2012

High Time-Resolved Cardiac Functional Imaging Using Temporal Regularization for Small Animal on a Clinical 3T Scanner

Dimitri Nestor Alice Van De Ville, Jean-Noël Hyacinthe

Accurate assessment of mice cardiac function with magnetic resonance imaging is essential for longitudinal studies and for drug development related to cardiovascular diseases. Whereas dedicated small animal MR scanners are not readily available, it would b ...

2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Michael Christoph Gastpar

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a limited number of noisy linear measurements is an important problem in compressed sensing. In the high-dimensional setting, it is known that recovery with a vanishing fraction ...

Institute of Electrical and Electronics Engineers2012

Sensing and Recovery under Sparsity Constraints

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Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

A Dual Algorithm For L1-Regularized Reconstruction Of Vector Fields

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Light Field Compressive Sensing In Camera Arrays

Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging

Light Field Compressive Sensing in Camera Arrays

High Time-Resolved Cardiac Functional Imaging Using Temporal Regularization for Small Animal on a Clinical 3T Scanner

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Light Field Compressive Sensing In Camera Arrays

A Dual Algorithm For L1-Regularized Reconstruction Of Vector Fields

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Multi-Way Compressed Sensing for Sparse Low-Rank Tensors

Light Field Compressive Sensing in Camera Arrays

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging

High Time-Resolved Cardiac Functional Imaging Using Temporal Regularization for Small Animal on a Clinical 3T Scanner

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm