Publications related to Acoustic DoA Estimation by One Unsophisticated Sensor

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

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

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

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

Morphological component analysis for sparse regularization in plane wave imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Eric Bezzam, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-p ...

Ieee2016

Total Variation Regularization of Shape Signals

Martin Kurt Storath

This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-va ...

IEEE2015

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

The open source dynamics in geospatial research and education

Stéphane Joost

Peer reviewing is one of the core processes of science. While the typical blind system helps to improve original submissions, there are opportunities for academic publishing to learn from open source practices (commits, bug reports, feature requests, docum ...

2014

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Michaël Unser, Stamatios Lefkimmiatis

Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupt ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sparse Distributed Learning Based on Diffusion Adaptation

Ali H. Sayed

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the d ...

IEEE2013

Stability and Hypothesis Transfer Learning

Ilja Kuzborskij

We consider the transfer learning scenario, where the learner does not have access to the source domain directly, but rather operates on the basis of hypotheses induced from it - the Hypothesis Transfer Learning (HTL) problem. Particularly, we conduct a th ...

2013

Acoustic DoA Estimation by One Unsophisticated Sensor

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

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Morphological component analysis for sparse regularization in plane wave imaging

Total Variation Regularization of Shape Signals

PURIFY: a new approach to radio-interferometric imaging

The open source dynamics in geospatial research and education

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Sparse Distributed Learning Based on Diffusion Adaptation

Stability and Hypothesis Transfer Learning

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

The open source dynamics in geospatial research and education

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Sparse Distributed Learning Based on Diffusion Adaptation

Total Variation Regularization of Shape Signals

Stability and Hypothesis Transfer Learning

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

Morphological component analysis for sparse regularization in plane wave imaging

PURIFY: a new approach to radio-interferometric imaging