Concept

Dimensional regularization

Related publications (32)

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Jean-Charles Baritaux

The recent advances in fluorescent molecular probes, photon detection instrumentation, and photon propagation models in tissue, have facilitated the emergence of innovative molecular imaging technologies such as Fluorescence Diffuse Optical Tomography (FDO ...

EPFL2012

Contribution to Integral Equation Techniques for Solving Electromagnetic Problems

Sergio Lopez Pena

Surface Mixed Potential Integral Equation (MPIE) formulations together with the Method of Moments (MoM) are widely used to solve electromagnetic problems. An accurate evaluation of the Green functions (GF) associated to the integral equation and of the cou ...

EPFL2010

Nonideal sampling and regularized interpolation of noisy data

Sathish Ramani

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

EPFL2009

TV-Regularized Generation of Planar Images from Omnicams

Pascal Frossard, Pierre Vandergheynst, Laurent Jacques, Didier Raboud

This paper addresses the problem of mapping images between different vision sensors. Such a mapping could be modeled as a sampling problem that has to encompass the change of geometry between the two sensors and the specific discretization of the real scen ...

2009

Nonideal Sampling and Regularization Theory

Michaël Unser, Dimitri Nestor Alice Van De Ville, Thierry Blu, Sathish Ramani

Shannon's sampling theory and its variants provide effective solutions to the problem of reconstructing a signal from its samples in some “shift-invariant” space, which may or may not be bandlimited. In this paper, we present some further justification for ...

IEEE2008

Deconvolution of 3D Fluorescence Micrographs with Automatic Risk Minimization

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter λ. For this, we consider a realistic noise model for data obtained ...

IEEE2008

Deconvolution of 3D fluorescence micrographs with automatic risk minimization

Michaël Unser, Cédric René Jean Vonesch, Sathish Ramani

We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter lambda. For this, we consider a realistic noise model for data obtai ...

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

Generalized Smoothing Splines and the Optimal Discretization of the Wiener Filter

Michaël Unser, Thierry Blu

We introduce an extended class of cardinal LL-splines, where L is a pseudo-differential operator satisfying some admissibility conditions. We show that the LL-spline signal interpolation problem is well posed and that its solution is the unique minimizer ...

IEEE2005

Computing the equivalent number of parameters of fixed-interval smoothers

Giancarlo Ferrari Trecate

The problem of reconstructing an unknown signal from

n

noisy samples can be addressed by means of nonparametric estimation techniques such as Tikhonov regularization, Bayesian regression and state-space fixed-interval smoothing. The practical use of thes ...

2001

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Computing the equivalent number of parameters of fixed-interval smoothers

Giancarlo Ferrari Trecate

The problem of reconstructing an unknown signal from

n

2001