compressive sensing | Laurent Jacques

Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy

Abstract: Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (biologic) sample can be coded before the FTI acquisition in a procedure called Coded Illumination-FTI (CI-FTI).

Amirafshar Moshtaghpour, Laurent Jacques

2018 2018 25th IEEE International Conference on Image Processing (ICIP)

Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models

Abstract: Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by using multiple, accurately chosen training signals to recover the missing information on the sensing model, an approach that can be resource-consuming and cumbersome.

Valerio Cambareri, Laurent Jacques

2018 Information and Inference

A quantized view on compressive sensing systems and compressive learning methods

A short introduction to compressive sensing, its applications and relatives

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

Abstract: 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 time, efficient data compression procedures, novel geometry-preserving embeddings or to estimate the information/bits stored in this reduced data representation.

Laurent Jacques, Valerio Cambareri

2017 Information and Inference

A Non-Convex Approach to Blind Calibration from Linear Sub-Gaussian Random Measurements

Abstract: Blind calibration is a bilinear inverse problem arising in modern sensing strategies, whose solution becomes crucial when traditional calibration aided by multiple, accurately designed training signals is either infeasible or resource-consuming.

Valerio Cambareri, Laurent Jacques

2016 Proceedings of the “37th WIC Symposium on Information Theory in the Benelux, 6th joint WIC/IEEE SP Symposium on Information Theory and Signal Processing in the Benelux”

Consistent Basis Pursuit for Signal and Matrix Estimates in Quantized Compressed Sensing

Abstract: This paper focuses on the estimation of low-complexity signals when they are observed through \(M\) uniformly quantized compressive observations. Among such signals, we consider 1-D sparse vectors, low-rank matrices, or compressible signals that are well approximated by one of these two models.

Amirafshar Moshtaghpour, Laurent Jacques, Valerio Cambareri, Kévin Degraux, Christophe De Vleeschouwer

2016 IEEE Signal Processing Letters

Quantized compressed sensing and quasi-isometric embeddings

Abstract: The advent of Compressed Sensing (CS) ten years ago has precipitated a radical re-thinking of signal acquisition, sensing, processing and transmission system design. A significant aspect of such systems is quantization (or digitization) of the acquired data before further processing or for the purpose of transmission and compression.

Compressed sensing, quantization and quasi-isometric embedding

Abstract: The advent of Compressed Sensing (CS) ten years ago has precipitated a radical re-thinking of signal acquisition, sensing, processing and transmission system design. A significant aspect of such systems is quantization (or digitization) of the acquired data before further processing or for the purpose of transmission and compression.