Publications related to Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

Jean-Philippe Thiran, Dimitri Nestor Alice Van De Ville, Yves Wiaux, Alessandro Daducci

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. F ...

Institute of Electrical and Electronics Engineers2014

Robust image reconstruction from multi-view measurements

Pierre Vandergheynst, Gilles Puy

We propose a novel method to accurately reconstruct a set of images representing a single scene from few linear multi-view measurements. Each observed image is modeled as the sum of a background image and a foreground one. The background image is common to ...

2014

On Sparsity Averaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

Recent developments in [1] and [2] introduced a novel regularization method for compressive imaging in the con- text of compressed sensing with coherent redundant dictionaries. The approach relies on the observation that natural images exhibit strong avera ...

2013

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

Sparsity averaging for radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

We propose a novel regularization method for compressive imaging in the context of the CS theory with coherent and redundant dictionaries. The approach relies on the conjecture that natural images exhibit strong average sparsity over multiple coherent fram ...

2013

Application of a New Sensing Principle for Photoacoustic Imaging of Point Absorbers

Dimitri Nestor Alice Van De Ville, Thierry Blu, Ivana Jovanovic, Zafer Dogan

Photoacoustic tomography (PAT) is a hybrid imaging method, which combines ultrasonic and optical imaging modalities, in order to overcome their respective weaknesses and to combine their strengths. It is based on the reconstruction of optical absorption pr ...

Spie-Int Soc Optical Engineering2013

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

Binary Compressed Imaging

Michaël Unser, Aurélien Bourquard

Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Sparsity-Inducing Reconstruction Methods for Fluorescence Diffuse Optical Tomography

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Sparsistency of $\ell_1$-Regularized $M$-Estimators

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

Robust image reconstruction from multi-view measurements

On Sparsity Averaging

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Sparsity averaging for radio-interferometric imaging

Application of a New Sensing Principle for Photoacoustic Imaging of Point Absorbers

Sparse Distributed Learning Based on Diffusion Adaptation

Binary Compressed Imaging

Jump-Sparse and Sparse Recovery Using Potts Functionals

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Sparse regularization for fiber ODF reconstruction: from the suboptimality of $\ell_2$ and $\ell_1$ priors to $\ell_0$

Sparsity averaging for radio-interferometric imaging

Sparse Distributed Learning Based on Diffusion Adaptation

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

On Sparsity Averaging

Robust image reconstruction from multi-view measurements

Application of a New Sensing Principle for Photoacoustic Imaging of Point Absorbers

Binary Compressed Imaging