Publications related to Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

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

Hard Thresholding with Norm Constraints

Volkan Cevher, Gilles Puy, Anastasios Kyrillidis

We introduce a new sparse recovery paradigm, called Normed Pursuits, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that Normed Pur ...

Ieee2012

JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we propose a novel and efficient model for compressed sensing of hyperspectral images. A large-size hyperspectral image can be subsampled by retaining only 3% of its original size, yet robustly recovered using the new approach we present here ...

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

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Pierre Vandergheynst, Mohammad Golbabaee

We propose a novel approach to reconstruct Hyperspectral images from very few number of noisy compressive measure- ments. Our reconstruction approach is based on a convex minimiza- tion which penalizes both the nuclear norm and the l2,1 mixed-norm of the d ...

Ieee2012

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

Jean-Philippe Thiran, Xavier Bresson, Dominique Zosso

In this paper we present an efficient numerical scheme for the recently introduced Geodesic Active Fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, data-te ...

2011

Compressive sensing meets game theory

Volkan Cevher, Sina Jafarpour

We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in under-determined linear regression problems. Given ƒ = Φα* + μ, the MUSE provably and efficiently finds a k-sparse vector α̂ such that ∥Φα̂ − ƒ∥∞ ≤ ∥ ...

IEEE2011

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Pierre Vandergheynst, Mohammad Golbabaee

Assume a multichannel data matrix, which due to the column-wise dependencies, has low-rank and joint-sparse representation. This matrix wont have many degrees of freedom. Enormous developments over the last decade in areas of compressed sensing and low-ran ...

2011

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

We study the sparsity of spectro-temporal representation of speech in reverberant acoustic conditions. This study motivates the use of structured sparsity models for efﬁcient speech recovery. We formulate the underdetermined convolutive speech separation i ...

2011

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

We study the sparsity of spectro-temporal representation of speech in reverberant acoustic conditions. This study motivates the use of structured sparsity models for efficient speech recovery. We formulate the underdetermined convolutive speech separation ...

2011

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

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Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Hard Thresholding with Norm Constraints

JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

Compressive sensing meets game theory

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hard Thresholding with Norm Constraints

Hyperspectral Image Compressed Sensing Via Low-Rank And Joint-Sparse Matrix Recovery

JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Guaranteed recovery of a low-rank and joint-sparse matrix from incomplete and noisy measurements

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

Compressive sensing meets game theory

Multi-party Speech Recovery Exploiting Structured Sparsity Models