Concept

Well-posed problem

Related publications (77)

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Pierre Vandergheynst, Alexandre Massoud Alahi, Laurent Jacques

In this paper, we propose to study the problem of localization of a dense set of people with a network of heterogeneous cameras. We propose to recast the problem as a linear inverse problem. The proposed framework is generic to any scene, scalable in the n ...

Springer Verlag2011

Compressed sensing for radio interferometric imaging: review and future

Yves Wiaux, Jason Douglas McEwen

Radio interferometry is a powerful technique for astronomical imaging. The theory of compressed sensing (CS) has been applied recently to the ill-posed inverse problem of recovering images from the measurements taken by radio interferometric telescopes. We ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Efficient Image Reconstruction Under Sparsity Constraints With Application To Mri And Bioluminescence Tomography

Michaël Unser, Matthieu Guerquin-Kern, Jean-Charles Baritaux

Most bioimaging modalities rely on indirect measurements of the quantity under investigation. The image is obtained as the result of an optimization problem involving a physical model of the measurement system. Due to the ill-posedness of the above problem ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Plenoptic based super-resolution for omnidirectional image sequences

Pascal Frossard, Pierre Vandergheynst, Luigi Bagnato

This paper addresses the reconstruction of high resolution omnidirectional images from a low resolution video acquired by an omnidirectional camera moving in a static scene. In order to exploit the additional information provided by the side images in the ...

2010

Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles

Victor Panaretos, John Maddocks, David Kraus

Given two samples of continuous zero-mean iid Gaussian processes on [0,1], we consider the problem of testing whether they share the same covariance structure. Our study is motivated by the problem of determining whether the mechanical properties of short ...

2010

An Efficient Numerical Method for General

Michaël Unser, Jean-Charles Baritaux, Kai Hassler

Reconstruction algorithms for fluorescence tomography have to address two crucial issues : 1) the ill-posedness of the reconstruction problem, 2) the large scale of numerical problems arising from imaging of 3-D samples. Our contribution is the design and ...

IEEE2010

Recursive Risk Estimation For Non-Linear Image Deconvolution With A Wavelet-Domain Sparsity Constraint

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We propose, a recursive data driven risk estimation method for non-linear iterative: deconvolution. Our two main contributions are 1) a solution domain risk-estimation approach that is applicable to nonlinear restoration algorithms for ill-conditioned inve ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Consistent and Regularized Magnification of Images

Michaël Unser, Philippe Thévenaz, Aurélien Bourquard, Katarina Balac

Because more output data must be created than is available from the input, magnification is an ill-posed problem. Traditional magnification relies on resampling an interpolation model at the appropriate rate; unfortunately, this simple solution is blind to ...

IEEE2008

Recursive Risk Estimation For Non-Linear Image Deconvolution With A Wavelet-Domain Sparsity Constraint

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We propose a recursive data-driven risk-estimation method for non-linear iterative deconvolution. Our two main contributions are 1) a solution-domain risk-estimation approach that is applicable to non-linear restoration algorithms for ill-conditioned inver ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Recursive Risk Estimation for Non-Linear Image Deconvolution with a Wavelet-Domain Sparsity Constraint

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

IEEE2008

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