Personne

Paul Hurley

Publications associées (54)

pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration

Martin Vetterli, Paul Hurley, Eric Bezzam, Sepand Kashani, Matthieu Martin Jean-André Simeoni

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a more natural choi ...

2022

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

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Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

2020

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The FFT algorithm is a key pillar of modern numerical computing. This document is a collection of working notes on FFT-based algorithms. Efficient implementations of the former are made available through the pyFFS package. ...

2020

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While restoration methods have been extensively studied in ultrasound (US) imaging, only few recent works have focused on modeling and understanding the blur from a physical point of view even in simple configurations, such as lossless homogeneous media. D ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

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We propose a recurrent neural-network for real-time reconstruction of acoustic camera spherical maps. The network, dubbed DeepWave, is both physically and algorithmically motivated: its recurrent architecture mimics iterative solvers from convex optimisati ...

2019

Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

Paul Hurley, Matthieu Martin Jean-André Simeoni

The starting point for deconvolution methods in radioastronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the in ...

IEEE2019

A Functional Framework For Ultrasound Imaging

Paul Hurley, Matthieu Martin Jean-André Simeoni

Delay-And-Sum (DAS), the state-of-art in ultrasound imaging, is known to be sub-optimal, resulting in low resolution and contrast. Most proposed improvements involve ad-hoc re-weighting, or hit computational bottlenecks given real-time requirements. This p ...

IEEE2018

Towards More Accurate Radio Telescope Images

Paul Hurley, Matthieu Martin Jean-André Simeoni, Nezihe Merve Gürel

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. We propose instead a method to remove the noise explicitly before ima ...

IEEE2018

A Physical Model of Non-stationary Blur in Ultrasound Imaging

Jean-Philippe Thiran, Yves Wiaux, Paul Hurley, Dimitris Perdios, Matthieu Martin Jean-André Simeoni, Marcel Arditi, Adrien Georges Jean Besson

Conventional ultrasound (US) imaging relies on delay-and-sum (DAS) beamforming which retrieves a radio- frequency (RF) image, a blurred estimate of the tissue reflectivity function (TRF). Despite the non-stationarity of the blur induced by propagation effe ...

2018

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