Nicolas Dobigeon

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IRIT ∙ ENSEEIHT

Professor, IRIT/INP-ENSEEIHT at University of Toulouse

Junior Member, Institut Universitaire de France (IUF)

AI Research Chair, Artificial and Natural Intelligence Toulouse Institute (ANITI)

Short biosketch

Nicolas Dobigeon received the Eng. degree in Electrical Engineering from INP-ENSEEIHT, Toulouse, France, and the M.Sc. degree in Signal Processing from the Institut National Polytechnique de Toulouse (Toulouse INP), both in June 2004. He received the Ph.D. degree and Habilitation à Diriger les Recherches in Signal Processing from Toulouse INP in 2007 and 2012, respectively. From 2007 to 2008, he was a Postdoctoral Research Associate with the Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor.

Since 2008, Nicolas Dobigeon has been with Toulouse INP (INP-ENSEEIHT, University of Toulouse) where he is currently a Professor. He conducts his research within the Signal and Communications (SC) group of IRIT and is an associate member of the Apprentissage Optimisation Complexité (AOC) project-team of CIMI. He currently holds an AI Research Chair at the Artificial and Natural Intelligence Toulouse Institute (ANITI) and he is a Junior Member of the Institut Universitaire de France (IUF, 2017-2022).

His recent research activities have been focused on statistical signal and image processing, with a particular interest in Bayesian inverse problems and applications to remote sensing, biomedical imaging and microscopy.

He is currently a member of the SPTM TC of the IEEE Signal Processing Society (since 2018), an Associate Editor for Signal Processing (since 2016) and Digital Signal Processing (since 2018) and the Deputy Head of the Dept. of Signals and Images at IRIT (since 2017).

He was a steering committee member of the French research group GDR-ISIS (2013-2018), a member of French National Council of Universities (CNU/Section 61, 2012-2016) and an elected member at the Research Council of Toulouse INP (2014-2016). He was the Head of the Major Program on "Signal and Image Processing" at INP-ENSEEIHT (2011-2019).

Webpage

http://dobigeon.perso.enseeiht.fr/

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Provably robust blind source separation of linear-quadratic near-separable mixtures

In this work, we consider the problem of blind source separation (BSS) b...
11/24/2020 ∙ by Christophe Kervazo, et al. ∙ 0 ∙ share

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High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm

Efficient sampling from a high-dimensional Gaussian distribution is an o...
10/04/2020 ∙ by Maxime Vono, et al. ∙ 0 ∙ share

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Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling

This paper discusses the reconstruction of partially sampled spectrum-im...
02/04/2020 ∙ by Étienne Monier, et al. ∙ 5 ∙ share

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Hyperspectral and multispectral image fusion under spectrally varying spatial blurs – Application to high dimensional infrared astronomical imaging

Hyperspectral imaging has become a significant source of valuable data f...
12/26/2019 ∙ by Claire Guilloteau, et al. ∙ 16 ∙ share

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Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images

Hyperspectral unmixing aims at identifying a set of elementary spectra a...
07/19/2019 ∙ by Adrien Lagrange, et al. ∙ 6 ∙ share

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Asymptotically exact data augmentation: models, properties and algorithms

Data augmentation, by the introduction of auxiliary variables, has becom...
02/15/2019 ∙ by Maxime Vono, et al. ∙ 0 ∙ share

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Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis

Supervised classification and representation learning are two widely use...
02/07/2019 ∙ by Adrien Lagrange, et al. ∙ 0 ∙ share

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Factor analysis of dynamic PET images: beyond Gaussian noise

Factor analysis has proven to be a relevant tool for extracting tissue t...
07/30/2018 ∙ by Yanna Cruz Cavalcanti, et al. ∙ 0 ∙ share

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Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images

Archetypal scenarios for change detection generally consider two images ...
07/21/2018 ∙ by Vinicius Ferraris, et al. ∙ 0 ∙ share

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Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity

Spectral variability is one of the major issue when conducting hyperspec...
04/30/2018 ∙ by Tatsumi Uezato, et al. ∙ 0 ∙ share

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Split-and-augmented Gibbs sampler - Application to large-scale inference problems

Recently, a new class of Markov chain Monte Carlo (MCMC) algorithms took...
04/16/2018 ∙ by Maxime Vono, et al. ∙ 0 ∙ share

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Robust fusion algorithms for unsupervised change detection between multi-band optical images - A comprehensive case study

Unsupervised change detection techniques are generally constrained to tw...
04/09/2018 ∙ by Vinicius Ferraris, et al. ∙ 0 ∙ share

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Reconstruction of partially sampled multi-band images - Application to STEM-EELS imaging

Electron microscopy has shown to be a very powerful tool to map the chem...
02/27/2018 ∙ by Étienne Monier, et al. ∙ 0 ∙ share

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Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning

Within a supervised classification framework, labeled data are used to l...
12/01/2017 ∙ by Adrien Lagrange, et al. ∙ 0 ∙ share

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Bayesian nonparametric Principal Component Analysis

Principal component analysis (PCA) is very popular to perform dimension ...
09/17/2017 ∙ by Clément Elvira, et al. ∙ 0 ∙ share

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Unmixing dynamic PET images with variable specific binding kinetics

To analyze dynamic positron emission tomography (PET) images, various ge...
07/19/2017 ∙ by Yanna Cruz Cavalcanti, et al. ∙ 0 ∙ share

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Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection

Archetypal scenarios for change detection generally consider two images ...
09/20/2016 ∙ by Vinicius Ferraris, et al. ∙ 0 ∙ share

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Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach

Change detection is one of the most challenging issues when analyzing re...
09/20/2016 ∙ by Vinicius Ferraris, et al. ∙ 0 ∙ share

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Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising

Piecewise constant denoising can be solved either by deterministic optim...
08/27/2016 ∙ by Jordan Frecon, et al. ∙ 0 ∙ share

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R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

This paper proposes a robust fast multi-band image fusion method to merg...
04/06/2016 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Multi-Band Image Fusion Based on Spectral Unmixing

This paper presents a multi-band image fusion algorithm based on unsuper...
03/29/2016 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Bayesian anti-sparse coding

Sparse representations have proven their efficiency in solving a wide cl...
12/18/2015 ∙ by Clément Elvira, et al. ∙ 0 ∙ share

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Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability

Hyperspectral unmixing is aimed at identifying the reference spectral si...
10/20/2015 ∙ by Pierre-Antoine Thouvenin, et al. ∙ 0 ∙ share

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Fast Spectral Unmixing based on Dykstra's Alternating Projection

This paper presents a fast spectral unmixing algorithm based on Dykstra'...
05/07/2015 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, cruci...
04/22/2015 ∙ by Nelly Pustelnik, et al. ∙ 0 ∙ share

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Hyperspectral pansharpening: a review

Pansharpening aims at fusing a panchromatic image with a multispectral o...
04/17/2015 ∙ by Laetitia Loncan, et al. ∙ 0 ∙ share

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Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

This paper proposes a fast multi-band image fusion algorithm, which comb...
02/10/2015 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation

Texture characterization is a central element in many image processing a...
10/17/2014 ∙ by Sébastien Combrexelle, et al. ∙ 0 ∙ share

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Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

This paper presents a variational based approach to fusing hyperspectral...
09/19/2014 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization

This paper introduces a robust mixing model to describe hyperspectral da...
01/22/2014 ∙ by Cédric Févotte, et al. ∙ 0 ∙ share

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Joint Bayesian estimation of close subspaces from noisy measurements

In this letter, we consider two sets of observations defined as subspace...
10/01/2013 ∙ by Olivier Besson, et al. ∙ 0 ∙ share

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Bayesian Fusion of Multi-Band Images

In this paper, a Bayesian fusion technique for remotely sensed multi-ban...
07/23/2013 ∙ by Qi Wei, et al. ∙ 0 ∙ share

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Nonlinear unmixing of hyperspectral images: models and algorithms

When considering the problem of unmixing hyperspectral images, most of t...
04/06/2013 ∙ by Nicolas Dobigeon, et al. ∙ 0 ∙ share

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Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM

We present a variational Bayesian method of joint image reconstruction a...
03/15/2013 ∙ by Se Un Park, et al. ∙ 0 ∙ share

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Nonlinear spectral unmixing of hyperspectral images using Gaussian processes

This paper presents an unsupervised algorithm for nonlinear unmixing of ...
07/23/2012 ∙ by Yoann Altmann, et al. ∙ 0 ∙ share

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Semi-blind Sparse Image Reconstruction with Application to MRFM

We propose a solution to the image deconvolution problem where the convo...
03/21/2012 ∙ by Se Un Park, et al. ∙ 0 ∙ share

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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their ...
02/28/2012 ∙ by Jose M. Bioucas-Dias, et al. ∙ 0 ∙ share

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Minimum mean square distance estimation of a subspace

We consider the problem of subspace estimation in a Bayesian setting. Si...
01/18/2011 ∙ by Olivier Besson, et al. ∙ 0 ∙ share

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