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Learning Data Triage: Linear Decoding Works for Compressive MRI

Related publications (32)

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A Fast Hadamard Transform for Signals with Sub-linear Sparsity

Martin Vetterli, Robin Scheibler, Saeid Haghighatshoar

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an N dimensional signal with a K-sparse WHT, where N is a power of two and K = O(N^α), scales sub- linearly in N for some 0 < α < 1. Assuming a ...

2013

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012

Sensing ECG signals with variable pulse width finite rate of innovation

Gilles Baechler

Mobile health is gradually taking more importance in our society and the need of new power efficient devices acquiring biosignals for long periods of time is becoming substantial. In this thesis, we study the power reduction we could achieve on ECG sensing ...

2012

Diffractionless Fourier Domain Microscopy

Theo Lasser, Rainer Leitgeb, Cédric Blatter, Tilman Schmoll

Several methods have been proposed and realized to increase the lateral resolution in microscopy and ultimately to beat the actual diffraction limit as stated by Abbe. Structured illumination for example enhances the lateral resolution by virtually extendi ...

2011

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Model-Based Compressive Sensing for Signal Ensembles

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M < N inner products with random vectors and then recover the signal via a sparsity-seeki ...

2009

Recovery of Compressible Signals in Unions of Subspaces

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with All < N random vectors and then recover the signal via a sparsity-s ...

2009

Compressive Sampling of Pulse Trains : Spread the Spectrum !

Pierre Vandergheynst, Rémi Gribonval, Laurent Jacques

In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...

2009

Compressive sensing recovery of spike trains using a structured sparsity model

Volkan Cevher

The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K, M = O(K log(N/K)) ...

2009

Super-Resolution from Unregistered and Totally Aliased Signals Using Subspace Methods

Martin Vetterli, Luciano Sbaiz, Patrick Vandewalle

In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the A/D converter, etc. A low-pass filter is then often applied before the sampling operation to avoid aliasing. However ...

Institute of Electrical and Electronics Engineers2007