Publications related to Spread spectrum for compressed sensing techniques in magnetic resonance imaging

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

3D Bpconvnet To Reconstruct Parallel Mri

Michaël Unser, Kyong Hwan Jin

In recent years, compressed sensing techniques have been applied to the reconstruction of parallel magnetic resonance (MR) images. Particularly for 3D MR signal, it is crucial to acquire fewer samples to reduce the distortions caused by long-time acquisiti ...

IEEE2018

Sparse inverse problems for Fourier imaging

Anna Auria Rasclosa

Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain. The recent theory of Compressed Sensing exploits this property offering a powerful frame ...

EPFL2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Gyrotron physics from linear to chaotic regimes: experiment and numerical modeling

Falk Hans Braunmüller

Gyrotrons belong to the family of high-power coherent radiation sources known as Electron Cyclotron Masers (ECMs) and are based on the physical mechanism of the ECM-instability, converting electron rotational kinetic energy into coherent electromagnetic ra ...

EPFL2016

Reducing acquisition time for microstructure imaging with spatially-regularized global optimization

Jean-Philippe Thiran, Yves Wiaux, Erick Jorge Canales Rodriguez, David Paul Roger Romascano, Alessandro Daducci, Anna Auria Rasclosa, Tim Bjørn Dyrby

Microstructure imaging with diffusion MRI (dMRI) is usually based on fitting models to the data using voxelwise nonlinear procedures that (i) are time consuming and (ii) need long acquisitions. Recently, we introduced the AMICO framework to accelerate the ...

2016

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing framework in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on multiple unknown (but sparse) signals and formulate the ...

Institute of Electrical and Electronics Engineers2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

Sparsity in tensor optimization for optical-interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bispectrum measurements. We review our previous work, which reformulates this nonlinear problem in the framework of tensor recover ...

IEEE2014

Spread spectrum for compressed sensing techniques in magnetic resonance imaging

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Unified Theory for Recovery of Sparse Signals in a General Transform Domain

3D Bpconvnet To Reconstruct Parallel Mri

Sparse inverse problems for Fourier imaging

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Gyrotron physics from linear to chaotic regimes: experiment and numerical modeling

Reducing acquisition time for microstructure imaging with spatially-regularized global optimization

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

Jump-Sparse and Sparse Recovery Using Potts Functionals

Compressed Sensing of Memoryless Sources

Sparsity in tensor optimization for optical-interferometric imaging

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

3D Bpconvnet To Reconstruct Parallel Mri

Sparse inverse problems for Fourier imaging

Reducing acquisition time for microstructure imaging with spatially-regularized global optimization

Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Gyrotron physics from linear to chaotic regimes: experiment and numerical modeling

Compressed Sensing of Memoryless Sources

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsity in tensor optimization for optical-interferometric imaging