Search Results for author: Nicolas Dobigeon

Found 45 papers, 5 papers with code

On-the-fly spectral unmixing based on Kalman filtering

no code implementations22 Jul 2024 Hugues Kouakou, José Henrique de Morais Goulart, Raffaele Vitale, Thomas Oberlin, David Rousseau, Cyril Ruckebusch, Nicolas Dobigeon

This work introduces an on-the-fly (i. e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis.

RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference -- Application to pulsar observations

no code implementations21 Feb 2024 Xiao Zhang, Ismaël Cognard, Nicolas Dobigeon

Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered.

Astronomy Image Denoising

Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling

no code implementations19 Feb 2024 Elhadji C. Faye, Mame Diarra Fall, Nicolas Dobigeon

This paper introduces a Bayesian framework for image inversion by deriving a probabilistic counterpart to the regularization-by-denoising (RED) paradigm.

Bayesian Inference Data Augmentation +3

AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising

no code implementations1 Jul 2023 Min Zhao, Jie Chen, Nicolas Dobigeon

In this way, both the characteristics of the deep autoencoder based unmixing methods and priors provided by denoisers are merged into our well-designed framework to enhance the unmixing performance.

Denoising Hyperspectral Unmixing

Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems

no code implementations29 Jun 2023 Min Zhao, Nicolas Dobigeon, Jie Chen

More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution.

Image Inpainting

Normalizing flow sampling with Langevin dynamics in the latent space

no code implementations20 May 2023 Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais

Normalizing flows (NF) use a continuous generator to map a simple latent (e. g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.

Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference

1 code implementation21 Apr 2023 Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais

This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution.

Bayesian Inference Denoising

Probabilistic Simplex Component Analysis by Importance Sampling

no code implementations22 Feb 2023 Nerya Granot, Tzvi Diskin, Nicolas Dobigeon, Ami Wiesel

In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex component analysis (PRISM).

Sliced-Wasserstein normalizing flows: beyond maximum likelihood training

no code implementations12 Jul 2022 Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais

Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e. g., images) and their failing to detect out-of-distribution data.

Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows

1 code implementation4 Jul 2022 Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais

Each of these functions is associated to one sub-flow of the network, whose output provides intermediate steps of the transport between the original and target measures.

Vocal Bursts Valence Prediction

CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors

no code implementations2 Mar 2022 Jin-Ju Wang, Nicolas Dobigeon, Marie Chabert, Ding-Cheng Wang, Ting-Zhu Huang, Jie Huang

In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e. g., optical or radar).

Change Detection Earth Observation +1

Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures

no code implementations8 Dec 2020 Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon

In this work, we tackle the problem of hyperspectral (HS) unmixing by departing from the usual linear model and focusing on a Linear-Quadratic (LQ) one.

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

1 code implementation4 Oct 2020 Maxime Vono, Nicolas Dobigeon, Pierre Chainais

In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization.


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

1 code implementation4 Feb 2020 Etienne Monier, Thomas Oberlin, Nathalie Brun, Xiaoyan Li, Marcel Tencé, Nicolas Dobigeon

Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden.

Image Reconstruction

Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images

no code implementations19 Jul 2019 Adrien Lagrange, Mathieu Fauvel, Stéphane May, Nicolas Dobigeon

The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps.

Hyperspectral Unmixing

Asymptotically exact data augmentation: models, properties and algorithms

no code implementations15 Feb 2019 Maxime Vono, Nicolas Dobigeon, Pierre Chainais

In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms.

Data Augmentation

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

no code implementations7 Feb 2019 Adrien Lagrange, Mathieu Fauvel, Stéphane May, José Bioucas-Dias, Nicolas Dobigeon

The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.

Classification Clustering +3

Factor analysis of dynamic PET images: beyond Gaussian noise

no code implementations30 Jul 2018 Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Cédric Févotte, Simon Stute, Maria-Joao Ribeiro, Clovis Tauber

Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.

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

no code implementations21 Jul 2018 Vinicius Ferraris, Nicolas Dobigeon, Yanna Cavalcanti, Thomas Oberlin, Marie Chabert

This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions.

Change Detection Dictionary Learning

Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity

no code implementations30 Apr 2018 Tatsumi Uezato, Mathieu Fauvel, Nicolas Dobigeon

The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel.

Hyperspectral Unmixing

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

no code implementations9 Apr 2018 Vinicius Ferraris, Nicolas Dobigeon, Marie Chabert

Unsupervised change detection techniques are generally constrained to two multi-band optical images acquired at different times through sensors sharing the same spatial and spectral resolution.

Change Detection

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

no code implementations27 Feb 2018 Étienne Monier, Thomas Oberlin, Nathalie Brun, Marcel Tencé, Marta de Frutos, Nicolas Dobigeon

Electron microscopy has shown to be a very powerful tool to map the chemical nature of samples at various scales down to atomic resolution.

Bayesian nonparametric Principal Component Analysis

no code implementations17 Sep 2017 Clément Elvira, Pierre Chainais, Nicolas Dobigeon

The selection of the number of significant components is essential but often based on some practical heuristics depending on the application.

Clustering Dimensionality Reduction

Unmixing dynamic PET images with variable specific binding kinetics

no code implementations19 Jul 2017 Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon, Simon Stute, Maria Ribeiro, Clovis Tauber

Modeling the variability of the specific binding factor has a strong potential impact for dynamic PET image analysis.

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

no code implementations20 Sep 2016 Vinicius Ferraris, Nicolas Dobigeon, Qi Wei, Marie Chabert

To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e. g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images.

Change Detection

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

no code implementations27 Aug 2016 Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Herwig Wendt, Patrice Abry

Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.


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

no code implementations6 Apr 2016 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret, Jose Bioucas-Dias, Simon Godsill

This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.

Multi-Band Image Fusion Based on Spectral Unmixing

no code implementations29 Mar 2016 Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret, Marcus Chen, Simon Godsill

The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem.

Bayesian anti-sparse coding

no code implementations18 Dec 2015 Clément Elvira, Pierre Chainais, Nicolas Dobigeon

Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding.

Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability

no code implementations20 Oct 2015 Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret

Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel.

Hyperspectral Unmixing

Fast Single Image Super-Resolution

no code implementations1 Oct 2015 Ningning Zhao, Qi Wei, Adrian Basarab, Nicolas Dobigeon, Denis Kouame, Jean-Yves Tourneret

Specifically, an analytical solution can be obtained and implemented efficiently for the Gaussian prior or any other regularization that can be formulated into an $\ell_2$-regularized quadratic model, i. e., an $\ell_2$-$\ell_2$ optimization problem.

Image Super-Resolution

Fast Spectral Unmixing based on Dykstra's Alternating Projection

no code implementations7 May 2015 Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret

This paper presents a fast spectral unmixing algorithm based on Dykstra's alternating projection.

Hyperspectral pansharpening: a review

no code implementations17 Apr 2015 Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya

In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.


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

no code implementations10 Feb 2015 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret

This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image.

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

no code implementations17 Oct 2014 Sébastien Combrexelle, Herwig Wendt, Nicolas Dobigeon, Jean-Yves Tourneret, Steve McLaughlin, Patrice Abry

Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge.

Hyperspectral and Multispectral Image Fusion based on a Sparse Representation

no code implementations19 Sep 2014 Qi Wei, José Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret

This paper presents a variational based approach to fusing hyperspectral and multispectral images.

Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization

1 code implementation22 Jan 2014 Cédric Févotte, Nicolas Dobigeon

This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures.

Hyperspectral Unmixing

Joint Bayesian estimation of close subspaces from noisy measurements

no code implementations1 Oct 2013 Olivier Besson, Nicolas Dobigeon, Jean-Yves Tourneret

In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces.

Bayesian Fusion of Multi-Band Images

no code implementations23 Jul 2013 Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret

In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented.

Nonlinear unmixing of hyperspectral images: models and algorithms

no code implementations6 Apr 2013 Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José C. M. Bermudez, Stephen McLaughlin, Alfred O. Hero

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).


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