Publications related to Sensing and Recovery under Sparsity Constraints

Shapes from pixels

Martin Vetterli, Arash Amini, Loïc Arnaud Baboulaz, Mitra Fatemi

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies ...

Institute of Electrical and Electronics Engineers2016

Learning-Based Compressive Subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The problem of recovering a structured signal

\mathbf{x} \in \mathbb{C}^p

from a set of dimensionality-reduced linear measurements

\mathbf{b} = \mathbf {A}\mathbf {x}

arises in a variety of applications, such as medical imaging, spectroscopy, Fourier o ...

Ieee-Inst Electrical Electronics Engineers Inc2016

A compressed beamforming framework for ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson, Olivier Bernard

Classical beamforming methods, based on Delay- And-Sum (DAS) require an extensive number of samples and delay calculations to obtain high-quality images. Compressed Beamforming (CB) proposes an alternative to DAS, based on compressed sensing, which aims at ...

Ieee2016

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

We consider design of sparse controllers for a stochastic linear system with infinite horizon quadratic objective. We formulate the non-sparse optimal solution through a semidefinite program for the second order moments of the states and inputs. Given that ...

University of Minnesota2016

Learning Data Triage: Linear Decoding Works for Compressive MRI

Volkan Cevher, Yen-Huan Li

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for ...

Ieee2016

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Kyong Hwan Jin

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general fr ...

IEEE2016

Seismic Simultaneous Source Separation via Patchwise Sparse Representation

Pascal Frossard, Yiren Zhou

The concept of simultaneous source has recently become of interest in seismic exploration, due to its efficient or economic acquisition or both. The blended data overlapped between shot records are acquired in simultaneous source acquisition. Separating th ...

Ieee-Inst Electrical Electronics Engineers Inc2016

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

Masih Nilchian, Marco Stampanoni

Reconstruction of underconstrained tomographic data sets remains a major challenge. Standard analytical techniques frequently lead to unsatisfactory results due to insufficient information. Several iterative algorithms, which can easily integrate a priori ...

IEEE2016

Optimality of Operator-Like Wavelets for Representing Sparse AR(1) Processes

Michaël Unser, Pedram Pad

The discrete cosine transform (DCT) is known to be asymptotically equivalent to the Karhunen-Loève transform (KLT) of Gaussian first-order auto-regressive (AR(1)) processes. Since being uncorrelated under the Gaussian hypothesis is synonymous with independ ...

IEEE2015

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

EPFL2015

Sensing and Recovery under Sparsity Constraints

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Shapes from pixels

Learning-Based Compressive Subsampling

A compressed beamforming framework for ultrafast ultrasound imaging

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

Learning Data Triage: Linear Decoding Works for Compressive MRI

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Seismic Simultaneous Source Separation via Patchwise Sparse Representation

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

Optimality of Operator-Like Wavelets for Representing Sparse AR(1) Processes

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

‪Stochastic Control-A Moment Approach to Sparse Control Design‬

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Shapes from pixels

Learning Data Triage: Linear Decoding Works for Compressive MRI

A compressed beamforming framework for ultrafast ultrasound imaging

Learning-Based Compressive Subsampling

Seismic Simultaneous Source Separation via Patchwise Sparse Representation

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

Optimality of Operator-Like Wavelets for Representing Sparse AR(1) Processes