
Provably robust blind source separation of linearquadratic nearseparable mixtures
In this work, we consider the problem of blind source separation (BSS) b...
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Highdimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
Efficient sampling from a highdimensional Gaussian distribution is an o...
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Fast reconstruction of atomicscale STEMEELS images from sparse sampling
This paper discusses the reconstruction of partially sampled spectrumim...
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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...
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Matrix cofactorization for joint spatialspectral unmixing of hyperspectral images
Hyperspectral unmixing aims at identifying a set of elementary spectra a...
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Asymptotically exact data augmentation: models, properties and algorithms
Data augmentation, by the introduction of auxiliary variables, has becom...
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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...
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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...
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Coupled dictionary learning for unsupervised change detection between multisensor remote sensing images
Archetypal scenarios for change detection generally consider two images ...
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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...
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Splitandaugmented Gibbs sampler  Application to largescale inference problems
Recently, a new class of Markov chain Monte Carlo (MCMC) algorithms took...
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Robust fusion algorithms for unsupervised change detection between multiband optical images  A comprehensive case study
Unsupervised change detection techniques are generally constrained to tw...
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Reconstruction of partially sampled multiband images  Application to STEMEELS imaging
Electron microscopy has shown to be a very powerful tool to map the chem...
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Hierarchical Bayesian image analysis: from lowlevel modeling to robust supervised learning
Within a supervised classification framework, labeled data are used to l...
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Bayesian nonparametric Principal Component Analysis
Principal component analysis (PCA) is very popular to perform dimension ...
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Unmixing dynamic PET images with variable specific binding kinetics
To analyze dynamic positron emission tomography (PET) images, various ge...
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Robust Fusion of MultiBand Images with Different Spatial and Spectral Resolutions for Change Detection
Archetypal scenarios for change detection generally consider two images ...
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Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a FusionBased Approach
Change detection is one of the most challenging issues when analyzing re...
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Bayesian selection for the l2Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optim...
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RFUSE: Robust Fast Fusion of MultiBand Images Based on Solving a Sylvester Equation
This paper proposes a robust fast multiband image fusion method to merg...
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MultiBand Image Fusion Based on Spectral Unmixing
This paper presents a multiband image fusion algorithm based on unsuper...
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Bayesian antisparse coding
Sparse representations have proven their efficiency in solving a wide cl...
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Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
Hyperspectral unmixing is aimed at identifying the reference spectral si...
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Fast Spectral Unmixing based on Dykstra's Alternating Projection
This paper presents a fast spectral unmixing algorithm based on Dykstra'...
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Combining local regularity estimation and total variation optimization for scalefree texture segmentation
Texture segmentation constitutes a standard image processing task, cruci...
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Hyperspectral pansharpening: a review
Pansharpening aims at fusing a panchromatic image with a multispectral o...
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Fast Fusion of MultiBand Images Based on Solving a Sylvester Equation
This paper proposes a fast multiband image fusion algorithm, which comb...
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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...
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Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
This paper presents a variational based approach to fusing hyperspectral...
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Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
This paper introduces a robust mixing model to describe hyperspectral da...
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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...
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Bayesian Fusion of MultiBand Images
In this paper, a Bayesian fusion technique for remotely sensed multiban...
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Nonlinear unmixing of hyperspectral images: models and algorithms
When considering the problem of unmixing hyperspectral images, most of t...
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Variational Semiblind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
We present a variational Bayesian method of joint image reconstruction a...
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Nonlinear spectral unmixing of hyperspectral images using Gaussian processes
This paper presents an unsupervised algorithm for nonlinear unmixing of ...
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Semiblind Sparse Image Reconstruction with Application to MRFM
We propose a solution to the image deconvolution problem where the convo...
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse RegressionBased Approaches
Imaging spectrometers measure electromagnetic energy scattered in their ...
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Minimum mean square distance estimation of a subspace
We consider the problem of subspace estimation in a Bayesian setting. Si...
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Nicolas Dobigeon
verfied profile
Professor, IRIT/INPENSEEIHT 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 INPENSEEIHT, 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 (INPENSEEIHT, 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) projectteam 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, 20172022).
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 GDRISIS (20132018), a member of French National Council of Universities (CNU/Section 61, 20122016) and an elected member at the Research Council of Toulouse INP (20142016). He was the Head of the Major Program on "Signal and Image Processing" at INPENSEEIHT (20112019).