Publications associées à Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

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It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of the samples is known and that the only distortion present in the observations is ...

Ieee2017

Acoustic DoA Estimation by One Unsophisticated Sensor

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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 ...

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

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We present a theoretical analysis and comparison of the effect of

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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 ...

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A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

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Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

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For the radial energy-supercritical nonlinear wave equation

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3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

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Inverse problems with shot noise arise in many modern biomedical imaging applications. The main challenge is to obtain an estimate of the underlying specimen from measurements corrupted by Poisson noise. In this work, we propose an efficient framework for ...

IEEE2013

Poisson Image Reconstruction With Hessian Schatten-Norm Regularization

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Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Structure Tensor Total Variation

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Structure Tensor Total Variation

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

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

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

Large global solutions for energy supercritical nonlinear wave equations on $\R^{3+1}$

3D Poisson Microscopy Deconvolution With Hessian Schatten-Norm Regularization

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge

Improved Variational Denoising of Flow Fields with Application to Phase-Contrast MRI Data