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

Imaging from Echoes: On Inverse Problems in Ultrasound

Ultrasound (US) imaging is currently living a revolution. On the one hand, ultrafast US imaging, a novel way of acquiring and producing US images, has paved the way to several advanced imaging modes, e.g. shear-wave elastography, ultrafast Doppler imaging ...

EPFL2019

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look ...

EPFL2018

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast ...

2018

Implementation d'un algorithme de reduction de taille des reseaux de neurones

François Thierry M Marelli

This document describes the implementation of a neuron pruning method with pyTorch, and analyzes the results obtained by applying this method on convolutional and residual networks. The performance of the algorithm is measured in different test cases and w ...

Idiap2018

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Hoài-Minh Nguyên

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

Learning Tomography Assessed Using Mie Theory

Michaël Unser, Demetri Psaltis, Joowon Lim

In optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index (RI) of the object. The Rytov method relies on a single-scattering propagation model and is commonly used to reconstruct images. Recently, a rec ...

2018

Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

Ulugbek Kamilov

Generalized linear models, where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = A x, arise in a range of applications in nonlinear filtering and regression. Approximate message passing (AMP) methods, b ...

IEEE2017

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Michaël Unser, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies

Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to t ...

2017

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Martin Kurt Storath

Mumford-Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising and segmentation. Being both non-smooth and non-convex, they are ...

Springer2016

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

Graph Chatbot

Chat with Graph Search

Imaging from Echoes: On Inverse Problems in Ultrasound

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Implementation d'un algorithme de reduction de taille des reseaux de neurones

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Learning Tomography Assessed Using Mie Theory

Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Implementation d'un algorithme de reduction de taille des reseaux de neurones

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Imaging from Echoes: On Inverse Problems in Ultrasound

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Learning Tomography Assessed Using Mie Theory

Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography