Sparse non-negative super-resolution - simplified and stabilised

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Publications associées (28)

A Dual Algorithm for $ L _{ 1 } $ -Regularized Reconstruction of Vector Fields

Michaël Unser, Pouya Dehghani Tafti, Emrah Bostan

Recent advances in vector-field imaging have brought to the forefront the need for efficient denoising and reconstruction algorithms that take the physical properties of vector fields into account and can be applied to large volumes of data. With these req ...

IEEE2012

The importance of priors for l2 regularization and total variation methods in quantitative susceptibility mapping

Rolf Gruetter, Yves Wiaux, José Pedro Rebelo Ferreira Marques, Gilles Puy, Diana Khabipova

Phase imaging has been demonstrated to offer a good contrast between and within brain tissues at 7T[1] with iron and myelin concentration being amongst the main modulators of the observed contrast due to their para- and dia-magnetic properties.Phase imagin ...

2012

Directional Input Adaptation in Parametric Optimal Control Problems

Dominique Bonvin, Benoît Chachuat, Saurabh Anand Deshpande

This paper deals with input adaptation in dynamic processes in order to guarantee feasible and optimal operation despite the presence of uncertainty. The proposed adaptation consists in using the nominal optimal inputs and adding appropriately designed inp ...

Society for Industrial and Applied Mathematics2012

Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We present nonquadratic Hessian-based regularization methods that can be effectively used for image restoration problems in a variational framework. Motivated by the great success of the total-variation (TV) functional, we extend it to also include second- ...

IEEE2012

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Michaël Unser, Aurélien Bourquard, Stamatios Lefkimmiatis

We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional ...

Ieee2012

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Ulugbek Kamilov, Emrah Bostan

We introduce a new wavelet-based method for the implementation of Total-Variation-type denoising. The data term is least-squares, while the regularization term is gradient-based. The particularity of our method is to exploit a link between the discrete gra ...

IEEE Institute of Electrical and Electronics Engineers2012

A Signal Processing Approach to Generalized 1-D Total Variation

Dimitri Nestor Alice Van De Ville, Fikret Isik Karahanoglu, Ilker Bayram

Total variation (TV) is a powerful method that brings great benefit for edge-preserving regularization. Despite being widely employed in image processing, it has restricted applicability for 1-D signal processing since piecewise-constant signals form a rat ...

Institute of Electrical and Electronics Engineers2011

FAST TV-L1 OPTICAL FLOW FOR INTERACTIVITY

Pierre Vandergheynst, Johann Paratte, Gilles Puy, Emmanuel D'Angelo

Vision is a natural tool for human-computer interaction, since it pro- vides visual feedback to the user and mimics some human behaviors. It requires however the fast and robust computation of motion primi- tives, which remains a difficult problem. In this ...

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

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