Publications related to Sampling methods for parametric non-bandlimited signals

Sampling at unknown locations, with an application in surface retrieval

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

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Sampling and Exact Reconstruction of Pulses with Variable Width

Martin Vetterli, Adam James Scholefield, Loïc Arnaud Baboulaz, Gilles Baechler

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, ...

Institute of Electrical and Electronics Engineers2017

Learning-based subsampling

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Yen-Huan Li, Luca Baldassarre, Baran Gözcü

The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, represe ...

2017

Media item relighting technique

Martin Vetterli, Loïc Arnaud Baboulaz, Damien Firmenich, Niranjan Thanikachalam

The present invention concerns a method of method of relighting a media item comprising media elements. The method comprises, for at least some of the media elements: determining, in a first signal domain, a light transport function describing the appearan ...

2017

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Kyong Hwan Jin

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

IEEE2017

Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

2017

A Sampling Theory for Non-Decaying Signals

Michaël Unser, Quy Ha Nguyen

The classical assumption in sampling and spline theories is that the input signal is square-integrable, which prevents us from applying such techniques to signals that do not decay or even grow at infinity. In this paper, we develop a sampling theory for m ...

2017

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

MATHICSE2017

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Sampling methods for parametric non-bandlimited signals

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Sampling at unknown locations, with an application in surface retrieval

Sampling and Exact Reconstruction of Pulses with Variable Width

Learning-based subsampling

Media item relighting technique

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Multilevel weighted least squares polynomial approximation

A Sampling Theory for Non-Decaying Signals

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Shapes from Pixels

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Sampling and Exact Reconstruction of Pulses with Variable Width

Learning-based subsampling

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Sampling at unknown locations, with an application in surface retrieval

Shapes from Pixels

Multilevel weighted least squares polynomial approximation

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

A Sampling Theory for Non-Decaying Signals

Media item relighting technique