activity
20052024
most citedCompressed sensing imaging techniques for radio interferometry

268 citations · 591 across the 36 of their papers we have counts for

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Showing 2016Show all

6 papers · 1 filter

cs.IT20165 cited

Sparse Support Recovery with Non-smooth Loss Functions

Kévin Degraux, Gabriel Peyré, Jalal M. Fadili +1

In this paper, we study the support recovery guarantees of underdetermined sparse regression using the -norm as a regularizer and a non-smooth loss function for data fideli…

cs.CV20163 cited

Blind Deconvolution of PET Images using Anatomical Priors

Stéphanie Guérit, Adriana González, Anne Bol +2

Images from positron emission tomography (PET) provide metabolic information about the human body. They present, however, a spatial resolution that is limited by physical and instr…

cs.IT201616 cited

Improving the Correlation Lower Bound for Simultaneous Orthogonal Matching Pursuit

Jean-François Determe, Jérôme Louveaux, Laurent Jacques +1

The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint support of a set of sparse signals acquired under a multiple measurement vector model. Critical…

cs.IT2016

Time for dithering: fast and quantized random embeddings via the restricted isometry property

Laurent Jacques, Valerio Cambareri

Recently, many works have focused on the characterization of non-linear dimensionality reduction methods obtained by quantizing linear embeddings, e.g., to reach fast processing ti…

cs.CV2016

Multi-resolution Compressive Sensing Reconstruction

Adriana Gonzalez, Hong Jiang, Gang Huang +1

We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid. In this context, the reconstructed image is divided into multiple re…

cs.CV2016

Cell segmentation with random ferns and graph-cuts

Arnaud Browet, Christophe De Vleeschouwer, Laurent Jacques +3

The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image…