Publications related to Sparse non-negative super-resolution - simplified and stabilised

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

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

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 ...

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

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) functiona ...

IEEE2012

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, Gilles Puy, Emmanuel D'Angelo, Johann Paratte

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

An optimal first-order solver for the TV-$L_1$ optical flow problem

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

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

A Variational Model for Object Segmentation Using Boundary Information, Statistical Shape Prior and the Mumford-Shah Functional

Jean-Philippe Thiran, Pierre Vandergheynst, Xavier Bresson

In this paper, we propose a variational model to segment an object belonging to a given scale space using the active contour method, a geometric shape prior and the Mumford-Shah functional. We define an energy functional composed by three complementary ter ...

2004

Multiscale Variational Approach to Simultaneous Image Regularization and Segmentation

Pierre Vandergheynst

The problem of finding a piecewise smooth approximation of an original image while preserving edges is closely related to the image segmentation problem. Indeed, if we know the cartoon version of the original image, it is easier to perform an image partiti ...

IEEE2003

Sparse non-negative super-resolution - simplified and stabilised

Graph Chatbot

Chat with Graph Search

Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Hessian-Based Regularization For 3-D Microscopy Image Restoration

Hessian-Based Regularization for 3-D Microscopy Image Restoration

A Signal Processing Approach to Generalized 1-D Total Variation

FAST TV-L1 OPTICAL FLOW FOR INTERACTIVITY

An optimal first-order solver for the TV-$L_1$ optical flow problem

Plenoptic based super-resolution for omnidirectional image sequences

A Variational Model for Object Segmentation Using Boundary Information, Statistical Shape Prior and the Mumford-Shah Functional

Multiscale Variational Approach to Simultaneous Image Regularization and Segmentation

Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications

Hessian-Based Regularization for 3-D Microscopy Image Restoration

Hessian-Based Regularization For 3-D Microscopy Image Restoration

FAST TV-L1 OPTICAL FLOW FOR INTERACTIVITY

A Signal Processing Approach to Generalized 1-D Total Variation

An optimal first-order solver for the TV-$L_1$ optical flow problem

A Variational Model for Object Segmentation Using Boundary Information, Statistical Shape Prior and the Mumford-Shah Functional

Plenoptic based super-resolution for omnidirectional image sequences

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Multiscale Variational Approach to Simultaneous Image Regularization and Segmentation