Publications related to Compressed sensing

Binary Compressed Imaging

Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Enhanced Compressed Sensing Recovery with Level Set Normals

Jean-Philippe Thiran, Xavier Bresson, Virginia Estellers Casas

We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the i ...

2013

Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

Pierre Vandergheynst, Mahdad Hosseini Kamal

The effective representation of the structures in the multiview images is an important problem that arises in visual sensor networks. This paper presents a novel recovery scheme from compressive samples which exploit local and non-local correlated structur ...

2013

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Pierre Vandergheynst, Mahdad Hosseini Kamal

The effective representation of the structures in the multiview images is an important problem that arises in visual sensor networks. This paper presents a novel recovery scheme from compressive samples which exploit local and non-local correlated structur ...

Ieee2013

Fast and robust estimation of jointly sparse channels

Martin Vetterli, Ali Hormati, Yann Barbotin

A device and method for estimating multipath jointly sparse channels. The method comprises receiving a number K of signal components by a number P of receiving antennas, where P≧2. The method further comprises estimating the sparsity condition of the multi ...

2013

Sparsity averaging for radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

We propose a novel regularization method for compressive imaging in the context of the CS theory with coherent and redundant dictionaries. The approach relies on the conjecture that natural images exhibit strong average sparsity over multiple coherent fram ...

2013

Anisotropic Interpolation of Sparse Generalized Image Samples

Michaël Unser, Aurélien Bourquard

Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sparse Distributed Learning Based on Diffusion Adaptation

Ali H. Sayed

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the d ...

IEEE2013

On Sparsity Averaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

Recent developments in [1] and [2] introduced a novel regularization method for compressive imaging in the con- text of compressed sensing with coherent redundant dictionaries. The approach relies on the observation that natural images exhibit strong avera ...

2013

Compressed Optical Imaging

Aurélien Bourquard

We address the resolution of inverse problems where visual data must be recovered from incomplete information optically acquired in the spatial domain. The optical acquisition models that are involved share a common mathematical structure consisting of a l ...

EPFL2013

Compressed sensing

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Binary Compressed Imaging

Enhanced Compressed Sensing Recovery with Level Set Normals

Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Fast and robust estimation of jointly sparse channels

Sparsity averaging for radio-interferometric imaging

Anisotropic Interpolation of Sparse Generalized Image Samples

Sparse Distributed Learning Based on Diffusion Adaptation

On Sparsity Averaging

Compressed Optical Imaging

Sparse Distributed Learning Based on Diffusion Adaptation

Joint Low-rank and Sparse Light Field Modeling for Dense Multiview Data Compression

On Sparsity Averaging

Sparsity averaging for radio-interferometric imaging

Compressed Optical Imaging

Binary Compressed Imaging

Anisotropic Interpolation of Sparse Generalized Image Samples

Enhanced Compressed Sensing Recovery with Level Set Normals

Joint Low-Rank And Sparse Light Field Modelling For Dense Multiview Data Compression

Fast and robust estimation of jointly sparse channels