148 citations · 313 across the 11 of their papers we have counts for
12 papers
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps
Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti +4
Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produc…
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…
The Rare Eclipse Problem on Tiles: Quantised Embeddings of Disjoint Convex Sets
Valerio Cambareri, Chunlei Xu, Laurent Jacques
Quantised random embeddings are an efficient dimensionality reduction technique which preserves the distances of low-complexity signals up to some controllable additive and multipl…
Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models
Valerio Cambareri, Laurent Jacques
Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by us…
A Greedy Blind Calibration Method for Compressed Sensing with Unknown Sensor Gains
Valerio Cambareri, Amirafshar Moshtaghpour, Laurent Jacques
The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which…
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…
A Non-Convex Blind Calibration Method for Randomised Sensing Strategies
Valerio Cambareri, Laurent Jacques
The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that…
Consistent Basis Pursuit for Signal and Matrix Estimates in Quantized Compressed Sensing
Amirafshar Moshtaghpour, Laurent Jacques, Valerio Cambareri +2
This paper focuses on the estimation of low-complexity signals when they are observed through uniformly quantized compressive observations. Among such signals, we consider 1-D…
Generalized Inpainting Method for Hyperspectral Image Acquisition
K. Degraux, V. Cambareri, L. Jacques +3
A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters.…
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
L. Jacques, C. De Vleeschouwer, Y. Boursier +64
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…
On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis
Valerio Cambareri, Mauro Mangia, Fabio Pareschi +2
Despite the linearity of its encoding, compressed sensing may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dim…
Low-complexity Multiclass Encryption by Compressed Sensing
Valerio Cambareri, Mauro Mangia, Fabio Pareschi +2
The idea that compressed sensing may be used to encrypt information from unauthorised receivers has already been envisioned, but never explored in depth since its security may seem…