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Towards Trustworthy Deep Learning for Image Reconstruction

Related publications (52)

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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

Analytical Form Of Shepp-Logan Phantom For Parallel Mri

Michaël Unser, Dimitri Nestor Alice Van De Ville, Matthieu Guerquin-Kern

We present an analytical form of ground-truth k-space data for the 2-D Shepp-Logan brain phantom in the presence of multiple and non-homogeneous receiving coils. The analytical form allows us to conduct realistic simulations and validations of reconstructi ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Analytical Form of Shepp-Logan Phantom for Parallel MRI

Michaël Unser, Dimitri Nestor Alice Van De Ville, Matthieu Guerquin-Kern

IEEE2010

Learning and recovering 3D surface deformations

Mathieu Salzmann

Recovering the 3D deformations of a non-rigid surface from a single viewpoint has applications in many domains such as sports, entertainment, and medical imaging. Unfortunately, without any knowledge of the possible deformations that the object of interest ...

EPFL2009

Integrated Modeling And Reconstruction With Sparsity Constraints For fDOT

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

We present a numerical framework for Fluorescence Diffuse Optical Tomography (fDOT) that combines a forward model together with an iterative reconstruction procedure. Using rapid linear solvers, we derived an efficient reconstruction strategy for quadratic ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Integrated Modeling and Reconstruction with Sparsity Constraints for fDOT

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

IEEE2009

Ensembles for sequence learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

EPFL2007

Ensembles for Sequence Learning

Christos Dimitrakakis

École Polytechnique Fédérale de Lausanne2006

Variational Approach to Tomographic Reconstruction

Michaël Unser, Thierry Blu

We formulate the tomographic reconstruction problem in a variational setting. The object to be reconstructed is considered as a continuous density function, unlike in the pixel-based approaches. The measurements are modeled as linear operators (Radon trans ...

SPIE2001

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000