Publications related to An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Maximum likelihood bolometry for ASDEX upgrade experiments

Riccardo Rossi

Bolometry is an essential diagnostic for calculating the power balances and for the understanding of different physical aspects of tokamak experiments. The reconstruction method based on the Maximum Likelihood (ML) principle, developed initially for JET, h ...

Bristol2023

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Jan Sickmann Hesthaven, Niccolo' Discacciati

We propose a model order reduction technique to accurately approximate the behavior of multi-component systems without any a-priori knowledge of the coupled model. In the offline phase, we construct independent surrogate models by solving the local problem ...

WILEY2023

Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision

Silvestro Micera, Daniela De Luca

The COVID-19 pandemic outbreak is causing a dramatic worsening in the already complicated living conditions of blind and visually impaired individuals. Social distancing is the most effective strategy to limit virus spread, but is extremely difficult for b ...

IEEE2022

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

Nicolas Henri Bernard Flammarion

We consider the problem of sampling from a density of the form p(x) ? exp(-f (x) - g(x)), where f : Rd-+ R is a smooth function and g : R-d-+ R is a convex and Lipschitz function. We propose a new algorithm based on the Metropolis-Hastings framework. Under ...

MICROTOME PUBL2022

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Piecewise linear interface-capturing volume-of-fluid method in axisymmetric cylindrical coordinates

Andreas Pautz, Lubomír Bures, Yohei Sato

The paper describes a novel implementation of the piecewise linear interface-capturing volume-of-fluid method (PLIC-VOF) in axisymmetric cylindrical coordinates. The principal innovative feature involved in this work is that both the forward and inverse re ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Volkan Cevher, Cong Bang Vu

In this paper, we propose a novel splitting method for finding a zero point of the sum of two monotone operators where one of them is Lipschizian. The weak convergence the method is proved in real Hilbert spaces. Applying the proposed method to composite m ...

2020

Image reconstruction for novel time domain near infrared optical tomography: towards clinical applications

Edoardo Charbon, Chunmin Zhang, Scott Anthony Lindner, Martin Wolf, Jingjing Jiang

Near infrared optical tomography (NIROT) is an emerging modality that enables imaging the oxygenation of tissue, which is a biomarker of tremendous clinical relevance. Measuring in reflectance is usually required when NIROT is applied in clinical scenarios ...

2020

Image reconstruction method based on a trained non-linear mapping

Jean-Philippe Thiran, Dimitris Perdios, Marcel Arditi, Adrien Besson, Florian Martinez

The present invention concerns an array processing image reconstruction method comprising: receiving (13) a set of echo waveforms as measurements from the object of interest; defining (15) a measurement model linking an unknown image of the object to the m ...

2020

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Maximum likelihood bolometry for ASDEX upgrade experiments

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

A first-order primal-dual method with adaptivity to local smoothness

Piecewise linear interface-capturing volume-of-fluid method in axisymmetric cylindrical coordinates

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators

Image reconstruction for novel time domain near infrared optical tomography: towards clinical applications

Image reconstruction method based on a trained non-linear mapping

Piecewise linear interface-capturing volume-of-fluid method in axisymmetric cylindrical coordinates

A first-order primal-dual method with adaptivity to local smoothness

Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Maximum likelihood bolometry for ASDEX upgrade experiments

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Image reconstruction for novel time domain near infrared optical tomography: towards clinical applications

Image reconstruction method based on a trained non-linear mapping

An Efficient Sampling Algorithm for Non-smooth Composite Potentials

A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators