
The Separation Capacity of Random Neural Networks
Neural networks with random weights appear in a variety of machine learn...
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Compressive lensless endoscopy with partial speckle scanning
The lensless endoscope (LE) is a promising device to acquire in vivo ima...
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Asymmetric compressive learning guarantees with applications to quantized sketches
The compressive learning framework reduces the computational cost of tra...
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Morphological components analysis for circumstellar disks imaging
Recent developments in astronomical observations enable direct imaging o...
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Keep the phase! Signal recovery in phaseonly compressive sensing
We demonstrate that a sparse signal can be estimated from the phase of c...
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When compressive learning fails: blame the decoder or the sketch?
In compressive learning, a mixture model (a set of centroids or a Gaussi...
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One Bit to Rule Them All : Binarizing the Reconstruction in 1bit Compressive Sensing
This work focuses on the reconstruction of sparse signals from their 1b...
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Sketching Datasets for LargeScale Learning (long version)
This article considers "sketched learning," or "compressive learning," a...
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Breaking the waves: asymmetric random periodic features for lowbitrate kernel machines
Many signal processing and machine learning applications are built from ...
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Compressive Learning of Generative Networks
Generative networks implicitly approximate complex densities from their ...
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The importance of phase in complex compressive sensing
We consider the question of estimating a real lowcomplexity signal (suc...
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(l1,l2)RIP and Projected BackProjection Reconstruction for PhaseOnly Measurements
This letter analyzes the performances of a simple reconstruction method,...
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Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling with Haar Wavelet Basis
We investigate the problems of 1D and 2D signal recovery from subsampl...
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OneBit Sensing of LowRank and Bisparse Matrices
This note studies the worstcase recovery error of lowrank and bisparse...
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Compressive Classification (Machine Learning without learning)
Compressive learning is a framework where (so far unsupervised) learning...
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Proceedings of the fourth "international Traveling Workshop on Interactions between lowcomplexity data models and Sensing Techniques" (iTWIST'18)
The iTWIST workshop series aim at fostering collaboration between intern...
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Compressive Singlepixel Fourier Transform Imaging using Structured Illumination
Single Pixel (SP) imaging is now a reality in many applications, e.g., b...
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Compressive Sampling Approach for Image Acquisition with Lensless Endoscope
The lensless endoscope is a promising device designed to image tissues i...
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Compressive Hyperspectral Imaging: Fourier Transform Interferometry meets Single Pixel Camera
This paper introduces a singlepixel HyperSpectral (HS) imaging framewor...
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Taking the edge off quantization: projected back projection in dithered compressive sensing
Quantized compressive sensing (QCS) deals with the problem of representi...
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Quantized Compressive KMeans
The recent framework of compressive statistical learning aims at designi...
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Multilevel Illumination Coding for Fourier Transform Interferometry in Fluorescence Spectroscopy
Fourier Transform Interferometry (FTI) is an interferometric procedure f...
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Multispectral Compressive Imaging Strategies using FabryPérot Filtered Sensors
This paper introduces two acquisition device architectures for multispec...
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Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering
In Compressive Sensing theory and its applications, quantization of sign...
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Blind Deconvolution of PET Images using Anatomical Priors
Images from positron emission tomography (PET) provide metabolic informa...
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Multiresolution Compressive Sensing Reconstruction
We consider the problem of reconstructing an image from compressive meas...
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Cell segmentation with random ferns and graphcuts
The progress in imaging techniques have allowed the study of various asp...
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PostReconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
Improving the quality of positron emission tomography (PET) images, affe...
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Discriminative and Efficient Label Propagation on Complementary Graphs for MultiObject Tracking
Given a set of detections, detected at each time instant independently, ...
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Nonparametric PSF estimation from celestial transit solar images using blind deconvolution
Context: Characterization of instrumental effects in astronomical imagin...
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Compressive Imaging and Characterization of Sparse Light Deflection Maps
Light rays incident on a transparent object of uniform refractive index ...
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Compressive Schlieren Deflectometry
Schlieren deflectometry aims at characterizing the deflections undergone...
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From Bits to Images: Inversion of Local Binary Descriptors
Local Binary Descriptors are becoming more and more popular for image ma...
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Compressive Optical Deflectometric Tomography: A Constrained TotalVariation Minimization Approach
Optical Deflectometric Tomography (ODT) provides an accurate characteriz...
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A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representatio...
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Invariant Spectral Hashing of Image Saliency Graph
Image hashing is the process of associating a short vector of bits to an...
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Laurent Jacques
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Professor and F.R.S.FNRS Research Associate at Université catholique de Louvain