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Spread Spectrum For Compressed Sensing Techniques In Magnetic Resonance Imaging

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-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 measurements. Our reconstruction approach is based on a convex minimization which penalizes both the nuclear norm and the l(2,1) mixed-norm of the dat ...

Ieee2012

Equivalence of synthesis and atomic formulations of sparse recovery

Volkan Cevher, Mitra Fatemi, Shayan Dashmiz

Recovery of sparse signals from linear, dimensionality reducing measurements broadly fall under two well-known formulations, named the synthesis and the analysis a ́ la Elad et al. Recently, Chandrasekaran et al. introduced a new algorithmic sparse recover ...

Ieee2012

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2011

Efficient Sampling for Accelerated Diffusion Magnetic Resonance Imaging

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method that allows connectivity mapping of the brain. However, despite major advances in this field, accurate inference of these patterns and its applicability within a clinical context is still ...

2011

On Accelerated Hard Thresholding Methods for Sparse Approximation

Volkan Cevher

We propose and analyze acceleration schemes for hard thresholding methods with applications to sparse approximation in linear inverse systems. Our acceleration schemes fuse combinatorial, sparse projection algorithms with convex optimization algebra to pro ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2011

An Alps View of Sparse Recovery

Volkan Cevher

We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra o ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Spread spectrum for compressed sensing techniques in magnetic resonance imaging

Jean-Philippe Thiran, Rolf Gruetter, Pierre Vandergheynst, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Gilles Puy

Magnetic resonance imaging (MRI) probes signals through Fourier measurements. Accelerating the acquisition process is of major interest for various MRI applications. The recent theory of compressed sensing shows that sparse or compressible signals may be r ...

IEEE Signal Process. Soc.2010

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

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

2010

Sparse Image Representation by Directionlets

Martin Vetterli, Vladan Velisavljevic, Pier Luigi Dragotti

Despite the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation are limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertic ...

Elsevier2010