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Martin Vetterli

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Publications associées (262)

Encoding And Decoding Mixed Bandlimited Signals Using Spiking Integrate-And-Fire Neurons

Martin Vetterli, Adam James Scholefield, Karen Adam

Conventional sampling focuses on encoding and decoding bandlimited signals by recording signal amplitudes at known time points. Alternately, sampling can be approached using biologically-inspired schemes. Among these are integrate-and-fire time encoding ma ...

IEEE2020

Kinetic Euclidean Distance Matrices

Martin Vetterli, Ivan Dokmanic, Puoya Tabaghi

Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize point ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Martin Vetterli, Paul Hurley, Adrien Georges Jean Besson, Matthieu Martin Jean-André Simeoni

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

Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals

Martin Vetterli, Adam James Scholefield, Karen Adam

Traditional sampling involves encoding a signal through (time, value)-pairs. In contrast, time encoding machines (TEMs) characterize a signal by recording time points which depend on the integral of the signal over time. We study multi-channel TEMs where c ...

2019

DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

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

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

Multi-spectral image priniting method

Martin Vetterli, Yves Bellouard, Gilles Baechler, Arnaud Latty, Adam James Scholefield, Michalina Wanda Pacholska

The present disclosure concerns a multi-spectral image printing method. The method includes the steps of: - providing a multi-spectral image; - providing a material; - determining changes in a refractive index value of the material permitting to reproduce ...

2019

Bound and Conquer: Improving Triangulation by Enforcing Consistency

Martin Vetterli, Alireza Ghasemi, Adam James Scholefield

We study the accuracy of triangulation in multi-camera systems with respect to the number of cameras. We show that, under certain conditions, the optimal achievable reconstruction error decays quadratically as more cameras are added to the system. Furtherm ...

2019

On The Move: Localization With Kinetic Euclidean Distance Matrices

Martin Vetterli, Ivan Dokmanic, Puoya Tabaghi

In this paper, we propose kinetic Euclidean distance matrices (KEDMs) a new algebraic tool for localization of moving points from spatio temporal distance measurements. KEDMs are inspired by the well-known Euclidean distance matrices (EDM) which model stat ...

IEEE2019

Super Resolution Phase Retrieval for Sparse Signals

Martin Vetterli, Amina Chebira, Gilles Baechler, Juri Ranieri, Miranda Krekovic

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Martin Vetterli, Benjamin Bejar Haro

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on t ...

2018

Structure from sound with incomplete data

Martin Vetterli, Gilles Baechler, Ivan Dokmanic, Miranda Krekovic

In this paper, we consider the problem of jointly localizing a microphone array and identifying the direction of arrival of acoustic events. Under the assumption that the sources are in the far field, this problem can be formulated as a constrained low-ran ...

2018

Efficient Multi-dimensional Diracs Estimation with Linear Sample Complexity

Martin Vetterli, Thierry Blu, Hanjie Pan

Estimating Diracs in continuous two or higher dimensions is a fundamental problem in imaging. Previous approaches extended one dimensional methods, like the ones based on finite rate of innovation (FRI) sampling, in a separable manner, e.g., along the hori ...

2018

Learning Neural Connectivity from Firing Activity: Scalable Algorithms with Provable Guarantees

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’ ...

Springer New York LLC2018

Combining Range and Direction for Improved Localization

Martin Vetterli, Robin Scheibler, Gilles Baechler, Frederike Dümbgen, Golnooshsadat Elhami, Miranda Krekovic, Adam James Scholefield

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use ...

2018

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Golnooshsadat Elhami, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Martin Vetterli, Jean-Philippe Thiran, Dimitris Perdios, Hanjie Pan, Adrien Georges Jean Besson, Eric Bezzam

In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rat ...

IEEE2018

Omnidirectional bats, point-to-plane distances, and the price of uniqueness

Martin Vetterli, Ivan Dokmanic, Miranda Krekovic

We study simultaneous localization and mapping with a device that uses reflections to measure its distance from walls. Such a device can be realized acoustically with a synchronized collocated source and receiver; it behaves like a bat with no capacity for ...

Ieee2017

Apparatus for imaging a page of a book or other similar document

Martin Vetterli, Loïc Arnaud Baboulaz

The present invention relates to an apparatus for imaging a page of a book or other similar document comprising: - a table defining a support plane, the book being disposed on the table such that the page to image is substantially parallel to the support p ...

2017

LEAP: Looking beyond pixels with continuous-space EstimAtion of Point sources

Martin Vetterli, Paul Hurley, Thierry Blu, Hanjie Pan, Matthieu Martin Jean-André Simeoni

Context. Two main classes of imaging algorithms have emerged in radio interferometry: the CLEAN algorithm and its multiple variants, and compressed-sensing inspired methods. They are both discrete in nature, and estimate source locations and intensities on ...

2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

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