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20052023
most citedCompressed sensing imaging techniques for radio interferometry

268 citations · 1.4k across the 80 of their papers we have counts for

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Showing 2018Show all

16 papers · 1 filter

cs.IT2018

Proceedings of the fourth "international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques" (iTWIST'18)

Sandrine Anthoine, Yannick Boursier, Laurent Jacques

The iTWIST workshop series aim at fostering collaboration between international scientific teams for developing new theories, applications and generalizations of low-complexity mod…

eess.SP2018

An extreme bit-rate reduction scheme for 2D radar localization

Thomas Feuillen, Luc Vandendorpe, Laurent Jacques

In this paper, we further expand on the work in [1] that focused on the localization of targets in a 2D space using 1-bit dithered measurements coming from a 2 receiving antennae r…

cs.LG2018★ 2 cited

Compressive Classification (Machine Learning without learning)

Vincent Schellekens, Laurent Jacques

Compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it. We propose a compressive learning…

astro-ph.IM2018★ 8 cited

Reference-less algorithm for circumstellar disks imaging

Benoît Pairet, Faustine Cantalloube, Laurent Jacques

Circumstellar disks play a key role in the understanding of stellar systems. Direct imaging of such extended structures is a challenging task. Current post-processing techniques, f…

eess.SP2018

Quantity over Quality: Dithered Quantization for Compressive Radar Systems

Thomas Feuillen, Chunlei Xu, Jérôme Louveaux +2

In this paper, we investigate a trade-off between the number of radar observations (or measurements) and their resolution in the context of radar range estimation. To this end, we…

cs.CV2018★ 4 cited

Compressive Sampling Approach for Image Acquisition with Lensless Endoscope

Stéphanie Guérit, Siddharth Sivankutty, Camille Scotté +3

The lensless endoscope is a promising device designed to image tissues in vivo at the cellular scale. The traditional acquisition setup consists in raster scanning during which the…

cs.CV2018

Compressive Single-pixel Fourier Transform Imaging using Structured Illumination

Amirafshar Moshtaghpour, José M. Bioucas-Dias, Laurent Jacques

Single Pixel (SP) imaging is now a reality in many applications, e.g., biomedical ultrathin endoscope and fluorescent spectroscopy. In this context, many schemes exist to improve t…

astro-ph.IM2018★ 37 cited

STIM map: detection map for exoplanets imaging beyond asymptotic Gaussian residual speckle noise

Benoît Pairet, Faustine Cantalloube, Carlos A. Gomez Gonzalez +2

Direct imaging of exoplanets is a challenging task as it requires to reach a high contrast at very close separation to the star. Today, the main limitation in the high-contrast ima…

cs.CV2018★ 1 cited

Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera

Amirafshar Moshtaghpour, José M. Bioucas-Dias, Laurent Jacques

This paper introduces a single-pixel HyperSpectral (HS) imaging framework based on Fourier Transform Interferometry (FTI). By combining a space-time coding of the light illuminatio…

eess.SP2018★ 10 cited

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

Thomas Feuillen, Chunlei Xu, Luc Vandendorpe +1

We present a novel scheme allowing for 2D target localization using highly quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW) radar with two receiving a…

cs.IT2018★ 2 cited

Taking the edge off quantization: projected back projection in dithered compressive sensing

Chunlei Xu, Vincent Schellekens, Laurent Jacques

Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any pr…

cs.LG2018★ 24 cited

Quantized Compressive K-Means

Vincent Schellekens, Laurent Jacques

The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive data…

cs.IT2018

Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy

Amirafshar Moshtaghpour, Laurent Jacques

Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (b…

cs.CV2018

Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors

Kévin Degraux, Valerio Cambareri, Bert Geelen +2

This paper introduces two acquisition device architectures for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not…

cs.IT2018★ 2 cited

A Variable Density Sampling Scheme for Compressive Fourier Transform Interferometry

A. Moshtaghpour, L. Jacques, V. Cambareri +2

Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscop…

cs.IT2018★ 2 cited

Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Chunlei Xu, Laurent Jacques

Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a ma…