Publications related to Variational Approach to Tomographic Reconstruction

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Reconstruction of underconstrained tomographic data sets remains a major challenge. Standard analytical techniques frequently lead to unsatisfactory results due to insufficient information. Several iterative algorithms, which can easily integrate a priori ...

IEEE2016

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Michaël Unser, Masih Nilchian, Michael Thompson McCann

We present a 3D iterative reconstruction method for microtomography, where iterative methods have previously been limited by their high computational cost. Our preliminary results show that it improves reconstruction quality over the traditional slice-by-s ...

OSA2016

Proximity Operators for Phase Retrieval

Michaël Unser, Frédéric Courbin, Ferréol Arnaud Marie Soulez

We present a new formulation of a family of proximity operators that generalize the projector step for phase retrieval. These proximity operators for noisy intensity measurements can replace the classical "noise-free" projection in any projection-based alg ...

OSA2016

Structure Modeling of High Dimensional Data

Mahdad Hosseini Kamal

The digitization of our most common appliances has led to a literal data deluge, some- times referred to as Big Data. The ever increasing volume of data we generate, coupled with our desire to exploit it ever faster, forces us to come up with innovative da ...

EPFL2015

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

Pramod Rastogi, Rishikesh Dilip Kulkarni

A novel technique is proposed for obtaining unwrapped phase estimation from a highly noisy exponential phase field. In this technique, the interference phase is represented as a linear combination of linearly independent and pre-defined basis functions alo ...

Iop Publishing Ltd2015

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Jean-Philippe Thiran, Alessandro Daducci, Alia Lemkaddem

Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion MRI data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the ne ...

Institute of Electrical and Electronics Engineers2015

Structure Tensor Total Variation

Michaël Unser, Stamatios Lefkimmiatis

We introduce a novel generic energy functional that we employ to solve inverse imaging problems within a variational framework. The proposed regularization family, termed as structure tensor total variation (STV), penalizes the eigenvalues of the structure ...

SIAM2015

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Pierre Vandergheynst, Michael Bronstein

Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we propose ShapeNet, a generalization of the popular convolutional neural ...

2015

Sensing the real world

Juri Ranieri

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phe ...

EPFL2014

Variational Approach to Tomographic Reconstruction

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A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Proximity Operators for Phase Retrieval

Structure Modeling of High Dimensional Data

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Structure Tensor Total Variation

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds

Sensing the real world

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Slice-by-Slice versus Fully 3D Reconstruction for Parallel-Beam x-Ray Microtomography

Proximity Operators for Phase Retrieval

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Phase estimation from noisy phase fringe patterns using linearly independent basis functions

Sensing the real world

Structure Tensor Total Variation

Structure Modeling of High Dimensional Data

ShapeNet: Convolutional Neural Networks on Non-Euclidean Manifolds