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Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Publications associées (70)

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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 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

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

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

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

Deliverable 3.1: Report on Learning Structured Sparse Models

The aim of this work package (WP) is to explore approaches to learn structured sparse models, that is sparse models where the sparsity assumption seems not to be sufficient, or when there is hope to exploit some additional knowledge together with the spars ...

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

Online performance guarantees for sparse recovery

Volkan Cevher

A K*-sparse vector x* ∈ RN produces measurements via linear dimensionality reduction as u = Φx* +n, where Φ ∈ RM×N (M

2011

Spread Spectrum for Universal Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors ...

2011

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

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