no code implementations • 21 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.
no code implementations • 19 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.
no code implementations • 1 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.
no code implementations • 29 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.
no code implementations • 20 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.
1 code implementation • 21 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.
no code implementations • 22 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).
no code implementations • 12 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.
1 code implementation • 4 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.
no code implementations • 2 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).
no code implementations • 8 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.
no code implementations • 24 Nov 2020 • Christophe Kervazo, Nicolas Gillis, Nicolas Dobigeon
The BF is in turn shown to be robust to noise under easier-to-check and milder conditions than SNPALQ.
1 code implementation • 4 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.
Computation
1 code implementation • 4 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.
no code implementations • 26 Dec 2019 • Claire Guilloteau, Thomas Oberlin, Olivier Berné, Nicolas Dobigeon
Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades.
no code implementations • 19 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.
no code implementations • 15 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.
no code implementations • 7 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.
no code implementations • 30 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.
no code implementations • 21 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.
no code implementations • 30 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.
no code implementations • 9 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.
no code implementations • 27 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.
no code implementations • 1 Dec 2017 • Adrien Lagrange, Mathieu Fauvel, Stéphane May, Nicolas Dobigeon
Within a supervised classification framework, labeled data are used to learn classifier parameters.
no code implementations • 17 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.
no code implementations • 19 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.
no code implementations • 20 Sep 2016 • Vinicius Ferraris, Nicolas Dobigeon, Qi Wei, Marie Chabert
Change detection is one of the most challenging issues when analyzing remotely sensed images.
no code implementations • 20 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.
no code implementations • 27 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.
no code implementations • 6 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.
no code implementations • 29 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.
no code implementations • 18 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.
no code implementations • 20 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.
no code implementations • 1 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.
no code implementations • 7 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.
no code implementations • 22 Apr 2015 • Nelly Pustelnik, Herwig Wendt, Patrice Abry, Nicolas Dobigeon
Texture segmentation constitutes a standard image processing task, crucial to many applications.
no code implementations • 17 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.
no code implementations • 10 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.
no code implementations • 17 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.
no code implementations • 19 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.
1 code implementation • 22 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.
no code implementations • 1 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.
no code implementations • 23 Jul 2013 • Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret
In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented.
no code implementations • 6 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).