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  2024 (13)
On-the-fly spectral unmixing based on Kalman filtering. Kouakou, H.; de M. Goulard, J. H.; Vitale, R.; Oberlin, T.; Rousseau, D.; Ruckebusch, C.; and Dobigeon, N. Chemometrics and Intelligent Laboratory Systems, 255(105252). Dec. 2024.
On-the-fly spectral unmixing based on Kalman filtering [link]Paper   link   bibtex  
Normalizing flow sampling with Langevin dynamics in the latent space. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. Machine Learning. Sept. 2024.
Normalizing flow sampling with Langevin dynamics in the latent space [link]Paper   link   bibtex  
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. IEEE Trans. Image Processing, 33: 3496–3507. May 2024.
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference [link]Paper   link   bibtex  
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference – Application to pulsar observations. Zhang, X.; Cognard, I.; and Dobigeon, N. Astronomy and Computing, 47. April 2024.
RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference – Application to pulsar observations [link]Paper   link   bibtex  
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising. Zhao, M.; Chen, J.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 62. March 2024.
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising [link]Paper   link   bibtex  
Superpixels meet essential spectra for fast Raman hyperspectral microimaging. Optics Express, 32(1). Jan. 2024.
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Bayesian source separation. Moussaoui, S.; Duarte, L. T.; Dobigeon, N.; and Jutten, C. In Jutten, C.; Moussaoui, S.; and Duarte, L., editor(s), Source separation in physical-chemical sensing, of Electronics & Communications Engineering Series. John Wiley & Sons, U.K., 2024.
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On-the-fly spectral unmixing for real-time hyperspectral data analysis. Kouakou, H.; de M. Goulard, J. H.; Vitale, R.; Oberlin, T.; Rousseau, D.; Ruckebusch, C.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Helsinki, Finland, Dec. 2024.
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Bayesian formulation of regularization by denoising - Model and Monte Carlo sampling. Faye, E. C.; Fall, M. D.; Chetouani, A.; and Dobigeon, N. In Proc. IEEE Int. Workshop Multimedia Signal Processing (MMSP), West Lafayette, IN, USA, Oct. 2024. Top 10% Paper Award
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A deep neural network to restore pulsar dynamic spectra corrupted by radio frequency interferences. Zhang, X.; Cognard, I.; and Dobigeon, N. In Proc. SPIE Software and Cyberinfrastructure for Astronomy VIII, volume 131011, pages 918–922, Yokohama, Japan, July 2024.
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Influence of spatial noise on two-pass fast Raman hyperspectral microimaging based on essential information. Gillet, V.; Mabilleau, G.; Loumaigne, M.; Coic, L.; Vitale, R.; Oberlin, T.; Goulart, H.; Dobigeon, N.; Ruckebush, C.; and Rousseau, D. In Proc. Focus on Microscopy (FOM), Genoa, Italy, March 2024.
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Kalman filter based online spectral unmixing. Kouakou, H.; de M. Goulart, J. H.; Oberlin, T.; Rousseau, D.; Ruckebusch, C.; and N. Dobigeon, undefined In Actes du Colloque Chimiométrie, Nantes, France, 2024. in french
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  2023 (8)
Spatial-spectral multiscale sparse unmixing of hyperspectral images. Ince, T.; and Dobigeon, N. IEEE Geoscience and Remote Sensing Letters, 20: 5511605. Oct. 2023.
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Guided deep generative model-based spatial regularization for multiband imaging inverse problems. Zhao, M.; Chen, J.; and Dobigeon, N. IEEE Trans. Image Processing, 32: 5692–5704. Oct. 2023.
Guided deep generative model-based spatial regularization for multiband imaging inverse problems [link]Paper   link   bibtex  
Assessment of essential information in the Fourier domain to accelerate Raman hyperspectral microimaging. Coic, L.; Vitale, R.; Moreau, M.; Rousseau, D.; de M. Goulart, J. H.; Dobigeon, N.; and Ruckebusch, C. Analytical Chemistry, 95(42): 15419–15832. Oct. 2023.
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A fast spatial-spectral NMF for hyperspectral unmixing. Ince, T.; and Dobigeon, N. IEEE Geoscience and Remote Sensing Letters, 20(5505305). June 2023.
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Probabilistic simplex component analysis by importance sampling. Granot, N.; Diskin, T.; Dobigeon, N.; and Wiesel, A. IEEE Signal Processing Letters, 30: 683–687. June 2023.
Probabilistic simplex component analysis by importance sampling [link]Paper   link   bibtex  
Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images. Cavalcanti, Y. C.; Oberlin, T.; Ferraris, V.; Dobigeon, N.; Ribeiro, M.; and Tauber, C. Medical Image Analysis, 84(102689). Feb. 2023.
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Arqueo 2.0: New methods for archaeological surveys in mountain areas . Calastrenc, C.; Baleux, F.; Poirier, N.; Rendu, C.; Campmajo, P.; Dobigeon, N.; Mellado, N.; Marais-Sicre, C.; Bal, M.; Philippe, M.; and Llubes, M. Treballs d'Arqueologia, 26(6): 95–109. Dec. 2023.
Arqueo 2.0: New methods for archaeological surveys in mountain areas  [link]Paper   link   bibtex   1 download  
Méthode MCMC plug-and-play avec a priori génératif profond. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. In Actes du XXIXième Colloque GRETSI, Grenoble, France, 2023. in french
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  2022 (11)
Fast hyperspectral unmixing using a multiscale sparse regularization. Ince, T.; and Dobigeon, N. IEEE Geoscience and Remote Sensing Letters, 19(6015305). Oct. 2022.
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Weighted residual NMF with spatial regularization for hyperspectral unmixing. Ince, T.; and Dobigeon, N. IEEE Geoscience and Remote Sensing Letters, 19(6010705). June 2022.
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A 3D-CNN framework for hyperspectral unmixing with spectral variability. Zhao, M.; Shi, S.; Chen, J.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 60: 1–14. Jan. 2022.
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High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm. Vono, M.; Dobigeon, N.; and Chainais, P. SIAM Review, 64(1): 3–56. 2022.
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Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. In Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, Oct. 2022.
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Informed spatial regularizations for fast fusion of astronomical images. Guilloteau, C.; Oberlin, T.; Berné, O.; and Dobigeon, N. In Proc. IEEE Int. Conf. Image Processing (ICIP), Bordeaux, France, Oct. 2022.
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Learning optimal transport between two empirical distributions with normalizing flows. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. In Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Grenoble, France, Sept. 2022.
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A Bayesian estimation formulation to voxel-based lesion-symptom mapping. Fall, M. D.; Dobigeon, N.; and Auzou, P. In Proc. European Signal Processing Conf. (EUSIPCO), Belgrade, Serbia, Sept. 2022.
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Inspecter les montagnes autrement – Renouvellement méthodologique de la prospection archéologique des terrains d'altitude. Calastrenc, C.; Baleux, F.; Poirier, N.; Rendu, C.; Campmajo, P.; Dobigeon, N.; Mellado, N.; Marais-Sicre, C.; Bal, M.; Philippe, M.; and Llubes, M. In Actes des Rencontres Internationales d'Archéologie et d'Histoire, Nice, France, 2022. in french
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Approximation du transport optimal entre distributions empiriques par flux de normalisation. Coeurdoux, F.; Dobigeon, N.; and Chainais, P. In Actes du XXVIIIième Colloque GRETSI, Nancy, France, 2022. in french
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Weighted Residual NMF with Spatial Regularization for Hyperspectral Unmixing – Complementary results and supplementary materials. Ince, T.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, May 2022.
Weighted Residual NMF with Spatial Regularization for Hyperspectral Unmixing – Complementary results and supplementary materials [pdf]Paper   link   bibtex  
  2021 (3)
Provably robust blind source separation of linear-quadratic near-separable mixtures. Kervazo, C.; Gillis, N.; and Dobigeon, N. SIAM J. Imaging Sciences, 14(4): 1848–1889. 2021.
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Asymptotically exact data augmentation: models, properties and algorithms. Vono, M.; Dobigeon, N.; and Chainais, P. Journal of Computational and Graphical Statistics, 30(2): 335–348. 2021.
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Approche bayésienne à l'estimation de la zone du langage chez des patients ayant eu un AVC. Fall, M. D.; Dobigeon, N.; and Auzou, P. In Actes des 52ième Journées de Statistique de la Société Française de Statistique (SFdS), Nice, France, 2021. in french
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  2020 (14)
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs – Application to high dimensional infrared astronomical imaging. Guilloteau, C.; Oberlin, T.; Berné, O.; and Dobigeon, N. IEEE Trans. Computational Imaging, 6: 1362–1374. Sept. 2020.
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Robust fusion algorithms for unsupervised change detection between multi-band optical images – A comprehensive case study. Ferraris, V.; Dobigeon, N.; and Chabert, M. Information Fusion, 64: 293–317. Dec. 2020.
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Hierarchical sparse nonnegative matrix factorization for hyperspectral unmixing with spectral variability. Uezato, T.; Fauvel, M.; and Dobigeon, N. Remote Sensing, 12(14). July 2020.
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Simulated JWST datasets for multispectral and hyperspectral image fusion. Guilloteau, C.; Oberlin, T.; Berné, O.; Habart, É.; and Dobigeon, N. The Astronomical Journal, 160(1): 28. Jun. 2020.
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Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling. Monier, E.; Oberlin, T.; Brun, N.; Li, X.; Tencé, M.; and Dobigeon, N. Ultramicroscopy, 215(112993). Aug. 2020.
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Matrix cofactorization for joint representation learning and supervised classification – Application to hyperspectral image analysis. Lagrange, A.; Fauvel, M.; May, S.; Bioucas-Dias, J.; and Dobigeon, N. Neurocomputing, 385: 132–147. Apr. 2020.
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Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 58(7): 4915–4927. July 2020.
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Successive nonnegative projection algorithm for linear quadratic mixtures. Kervazo, C.; Gillis, N.; and Dobigeon, N. In Proc. European Signal Processing Conf. (EUSIPCO), Amsterdam, Netherlands, Sept. 2020.
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Fusion of hyperspectral and multispectral infrared astronomical images. Guilloteau, C.; Oberlin, T.; Berné, O.; and Dobigeon, N. In Proc. Workshop on Sensor Array and Multichannel Signal Processing (SAM), Hangzhou, China, June 2020.
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Unsupervised change detection for multimodal remote sensing images via coupled dictionary learning and sparse coding. Ferraris, V.; Dobigeon, N.; Cavalcanti, Y. C.; Oberlin, T.; and Chabert, M. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
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Supplementary materials: Hierarchical sparse nonnegative matrix factorization for hyperspectral unmixing with spectral variability. Uezato, T.; Fauvel, M.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, July 2020.
Supplementary materials: Hierarchical sparse nonnegative matrix factorization for hyperspectral unmixing with spectral variability [pdf]Paper   link   bibtex  
Robust fusion algorithms for unsupervised change detection between multi-band optical images – A comprehensive case study – Complementary results. Ferraris, V.; Dobigeon, N.; and Chabert, M. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, June 2020.
Robust fusion algorithms for unsupervised change detection between multi-band optical images – A comprehensive case study – Complementary results [pdf]Paper   link   bibtex  
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling – Complementary results. Monier, E.; Oberlin, T.; Brun, N.; Tencé, M.; Li, X.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Feb. 2020.
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling – Complementary results [pdf]Paper   link   bibtex  
Approches problèmes inverses pour la fusion d'images multi-bandes. Oberlin, T.; and Dobigeon, N. Technical Report CNES, Toulouse, France, June 2020.
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  2019 (22)
Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images. Ferraris, V.; Dobigeon, N.; Cavalcanti, Y. C.; Oberlin, T.; and Chabert, M. Computer Vision and Image Understanding, 189(102817). Dec. 2019.
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A Bayesian nonparametric model for unsupervised joint segmentation of a collection of images. Sodjo, J.; Giremus, A.; Dobigeon, N.; and Caron, F. IEEE Access, 7(1): 120176–120188. Dec. 2019.
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Non-linear unmixing of hyperspectral images using multiple-kernel self-organizing maps. Rashwan, S.; Dobigeon, N.; Sheta, W.; and Hassan, H. IET Image Processing, 13(12): 2190–2195. Oct. 2019.
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Factor analysis of dynamic PET images: beyond Gaussian noise. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Févotte, C.; Stute, S.; Ribeiro, M.; and Tauber, C. IEEE Trans. Med. Imaging, 38(9): 2231–2241. Sept. 2019.
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Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity. Uezato, T.; Fauvel, M.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 57(6): 3980–3992. June 2019.
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Partially asynchronous distributed unmixing of hyperspectral images. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Geoscience and Remote Sensing, 57(4): 2009-2021. April 2019.
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Split-and-augmented Gibbs sampler – Application to large-scale inference problems. Vono, M.; Dobigeon, N.; and Chainais, P. IEEE Trans. Signal Processing, 67(6): 1648–1661. Jan. 2019.
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Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. Pattern Recognition, 85: 26–36. Jan. 2019.
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Matrix cofactorization for joint unmixing and classification of hyperspectral images. Lagrange, A.; Fauvel, M.; May, S.; Bioucas-Dias, J. M.; and Dobigeon, N. In Proc. European Signal Processing Conf. (EUSIPCO), A Coruna, Spain, 2019.
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On variable splitting for Markov chain Monte Carlo. Vono, M.; Dobigeon, N.; and Chainais, P. In Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS), Toulouse, France, Sept. 2019.
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Reconstruction of partially sampled STEM-EELS images with atomic resolution. Monier, E.; Oberlin, T.; Brun, N.; and Dobigeon, N. In Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS), Toulouse, France, July 2019.
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Fusion of hyperspectral and multispectral infrared astronomical images. Guilloteau, C.; Oberlin, T.; Berné, O.; and Dobigeon, N. In Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS), Toulouse, France, July 2019.
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Bayesian image restoration under Poisson noise and log-concave prior. Vono, M.; Dobigeon, N.; and Chainais, P. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Brighton, U.K., April 2019.
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Efficient sampling through variable splitting-inspired Bayesian hierarchical models. Vono, M.; Dobigeon, N.; and Chainais, P. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Brighton, U.K., April 2019.
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Unmixing dynamic PET images: combining spatial heterogeneity and non-Gaussian noise. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Févotte, C.; Stute, S.; and Tauber, C. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), Brighton, U.K., April 2019.
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Cofactorisation de matrices pour le démélange et la classification conjoints d'images hyperspectrales. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. In Actes du XXVIIième Colloque GRETSI, Lille, France, 2019. in french
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Fusion d'images multispectrales et hyperspectrales pour l'observation en astronomie infrarouge. Guilloteau, C.; Oberlin, T.; Berné, O.; and Dobigeon, N. In Actes du XXVIIième Colloque GRETSI, Lille, France, 2019. in french
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Un modèle augmenté asymptotiquement exact pour la restauration bayésienne d'images dégradées par un bruit de Poisson. Vono, M.; Chainais, P.; and Dobigeon, N. In Actes du XXVIIième Colloque GRETSI, Lille, France, 2019. in french
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Modèles augmentés asymptotiquement exacts. Vono, M.; Chainais, P.; and Dobigeon, N. In Actes du XXVIIième Colloque GRETSI, Lille, France, 2019. in french
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Cofactorisation de matrices pour le démélange et la classification conjoints d'images hyperspectrales. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. In Actes du Colloque du groupe SFPT-GH, Toulouse, France, 2019. in french
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Reconstruction de spectres-images STEM-EELS partiellement échantillonnés. Monier, E.; Oberlin, T.; Brun, N.; Tencé, M.; de Frutos, M.; and Dobigeon, N. In Actes du XVIième Colloque de la Société Française des Microscopies (SFMu), pages 74–75, July 2019.
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Matrix cofactorization for joint representation learning and supervised classification – Application to hyperspectral image analysis – Complementary results. Lagrange, A.; Fauvel, M.; May, S.; Bioucas-Dias, J.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2019.
Matrix cofactorization for joint representation learning and supervised classification – Application to hyperspectral image analysis – Complementary results [pdf]Paper   link   bibtex  
  2018 (16)
Reconstruction of partially sampled multi-band images – Application to EELS microscopy. Monier, E.; Oberlin, T.; Brun, N.; Tencé, M.; de Frutos, M.; and Dobigeon, N. IEEE Trans. Computational Imaging, 4(4): 585–598. Dec. 2018.
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Unmixing dynamic PET images with variable specific binding kinetics. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Stute, S.; Ribeiro, M.; and Tauber, C. Medical Image Analysis, 49: 117–127. Oct. 2018.
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Hyperspectral image unmixing with LiDAR data-aided spatial regularization. Uezato, T.; Fauvel, M.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 56(2): 4098–4108. July 2018.
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Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach. Ferraris, V.; Dobigeon, N.; Wei, Q.; and Chabert, M. IEEE Trans. Geoscience and Remote Sensing, 56(3): 1566–1578. March 2018.
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Foreword to the Special Issue on Quality Improvements of Remote Sensing Data. Shen, H.; Jia, X.; Bioucas-Dias, J. M.; Dobigeon, N.; Cui, Y.; and Pacifici, F. IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing, 11(3): 687–690. March 2018.
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A hierarchical Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Computational Imaging, 4(1): 32–45. Jan. 2018.
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Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler. Vono, M.; Dobigeon, N.; and Chainais, P. In Proc. IEEE Int. Workshop Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, Sept. 2018. Finalist of the Best Student Paper Award
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Reconstruction of partially sampled EELS images. Monier, E.; Oberlin, T.; Brun, N.; de Frutos, M.; Tencé, M.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, Sept. 2018.
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A multiple endmember mixing model to handle spectral variability. Uezato, T.; Fauvel, M.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, Sept. 2018.
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A comparative study of fusion-based change detection methods for multi-band images with different spectral and spatial resolutions. Ferraris, V.; Yokoya, N.; Dobigeon, N.; and Chabert, M. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pages 5021–5024, Valencia, Spain, July 2018.
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LiDAR-driven spatial regularization for hyperspectral unmixing. Uezato, T.; Fauvel, M.; and Dobigeon, N. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pages 1740–1743, Valencia, Spain, July 2018.
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A Bayesian model for joint unmixing and robust classification of hyperspectral image. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3399–3403, Calgary, Canada, April 2018.
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Un modèle bayésien pour le démélange, la segmentation et la classification robuste d'images hyperspectrales. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. In Actes de la Conférence Française de Photogrammétrie et de Télédétection, Marne-la-Vallée, France, 2018. in french
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Factor analysis of dynamic PET images: beyond Gaussian noise – Complementary results and supporting materials. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Stute, S.; Ribeiro, M.; and Tauber, C. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2018.
Factor analysis of dynamic PET images: beyond Gaussian noise – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity – Complementary results. Uezato, T.; Fauvel, M.; May, S.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Apr. 2018.
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity – Complementary results [pdf]Paper   link   bibtex  
Débruitage d'images hyperspectrales. Oberlin, T.; Dobigeon, N.; and Chabert, M. Technical Report R&T16/OT-0006-037, Toulouse, France, April 2018.
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  2017 (23)
Bayesian selection for the $\ell-2$-Potts model regularization parameter: 1D piecewise constant signal denoising. Frecon, J.; Pustelnik, N.; Dobigeon, N.; Wendt, H.; and Abry, P. IEEE Trans. Signal Processing, 65(19): 5215–5224. Oct. 2017.
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A split-and-merge approach for hyperspectral band selection. Rashwan, S.; and Dobigeon, N. IEEE Geoscience and Remote Sensing Letters, 14(8): 1378–1382. Aug. 2017.
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Robust fusion of multi-band images with different spatial and spectral resolutions for change detection. Ferraris, V.; Dobigeon, N.; Wei, Q.; and Chabert, M. IEEE Trans. Computational Imaging, 3(2): 175–186. April 2017.
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Fast hyperspectral unmixing in presence of nonlinearity or mismodelling effects. Halimi, A.; Bioucas-Dias, J. M.; Dobigeon, N.; Buller, G. S.; and McLaughlin, S. IEEE Trans. Computational Imaging, 3(2): 146–159. April 2017.
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Bayesian anti-sparse coding. Elvira, C.; Chainais, P.; and Dobigeon, N. IEEE Trans. Signal Processing, 65(7): 1660–1672. April 2017.
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Unmixing Dynamic PET images with a PALM algorithm. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; and Tauber, C. In Proc. European Signal Processing Conf. (EUSIPCO), pages 425–429, Kos, Greece, Sept. 2017.
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Unmixing multitemporal hyperspectral images accounting for smooth and abrupt variations. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. In Proc. European Signal Processing Conf. (EUSIPCO), pages 2378–2382, Kos, Greece, Sept. 2017.
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Reconstruction of randomly and partially sampled STEM spectrum-images. Monier, E.; Oberlin, T.; Brun, N.; Tencé, M.; and Dobigeon, N. In Microscopy & MicroAnalysis (MM), volume 23, pages 170–171, Saint Louis, USA, Aug. 2017.
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Fast hyperspectral unmixing in presence of sparse multiple scattering nonlinearities. Halimi, A.; Bioucas-Dias, J. M.; Dobigeon, N.; Buller, G. S.; and McLaughlin, S. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3111–3115, New Orleans, USA, March 2017.
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Bayesian nonparametric subspace estimation. Elvira, C.; Chainais, P.; and Dobigeon, N. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 2247–2251, New Orleans, USA, March 2017. Student paper contest finalist
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Bayesian-driven criterion to automatically select the regularization parameter in the $\ell_1$-Potts model. Frecon, J.; Pustelnik, N.; Dobigeon, N.; Wendt, H.; and Abry, P. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3839–3843, New Orleans, USA, March 2017.
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Change detection between multi-band images using a robust fusion-based approach. Ferraris, V.; Dobigeon, N.; Wei, Q.; and Chabert, M. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3346–3350, New Orleans, USA, March 2017.
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A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images. Sodjo, J.; Giremus, A.; Dobigeon, N.; and Giovannelli, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 1882–1886, New Orleans, USA, March 2017.
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Sélection du paramètre de régularisation dans le problème l2-Potts. Frecon, J.; Pustelnik, N.; Dobigeon, N.; Wendt, H.; and Abry, P. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Reconstruction de spectres-images partiellement échantillonnés en microscopie EELS. Monier, E.; Oberlin, T.; Brun, N.; and Dobigeon, N. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Un modèle bayésien pour le démélange, la segmentation et la classification robuste d'images hyperspectrales. Lagrange, A.; Fauvel, M.; May, S.; and Dobigeon, N. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Démélange d'images TEP dynamiques. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Stute, S.; and Tauber, C. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Détection de changements par fusion robuste d'images multi-bandes de résolutions spatiale et spectrale différentes. Ferraris, V.; Dobigeon, N.; Wei, Q.; and Chabert, M. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Une formulation bayésienne du codage antiparcimonieux. Elvira, C.; Chainais, P.; and Dobigeon, N. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Une approche distribuée asynchrone pour la factorisation en matrices non-négatives – Application au démélange hyperspectral. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. In Actes du XXVIième Colloque GRETSI, Juan-les-Pins, France, 2017. in french
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Hyperspectral image unmixing with LiDAR data-aided spatial regularization – Complementary results. Uezato, T.; Fauvel, M.; May, S.; and Dobigeon, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2017.
Hyperspectral image unmixing with LiDAR data-aided spatial regularization – Complementary results [pdf]Paper   link   bibtex  
Unmixing dynamic PET images with variable specific binding kinetics – Supporting materials. Cavalcanti, Y. C.; Oberlin, T.; Dobigeon, N.; Stute, S.; Ribeiro, M.; and Tauber, C. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2017.
Unmixing dynamic PET images with variable specific binding kinetics – Supporting materials [pdf]Paper   link   bibtex  
A hierarchical Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images – Complementary results and supporting materials. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, May 2017.
A hierarchical Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images – Complementary results and supporting materials [pdf]Paper   link   bibtex  
  2016 (17)
Combining local regularity estimation and total variation optimization for scale-free texture segmentation. Pustelnik, N.; Wendt, H.; Abry, P.; and Dobigeon, N. IEEE Trans. Computational Imaging, 4(2): 468–479. Dec. 2016.
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Multi-Band Image Fusion Based on Spectral Unmixing. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; Tourneret, J.; Chen, M.; and Godsill, S. IEEE Trans. Geoscience and Remote Sensing, 54(12): 7236–7249. Dec. 2016.
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R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation. Wei, Q.; Dobigeon, N.; Tourneret, J.; Bioucas-Dias, J. M.; and Godsill, S. IEEE Signal Processing Letters, 23(11): 1632–1636. Nov. 2016.
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Online unmixing of multitemporal hyperspectral images accounting for spectral variability. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 25(9): 3979–3990. Sept. 2016.
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Fast single image super-resolution using a new analytical solution for $\ell_2-\ell_2$ problems. Zhao, N.; Wei, Q.; Basarab, A.; Dobigeon, N.; Kouamé, D.; and Tourneret, J. IEEE Trans. Image Processing, 25(8): 3683–3697. Aug. 2016.
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Detection and Correction of Glitches in a Multiplexed Multi-channel Data Stream – Application to the MADRAS Instrument. Wendt, H.; Dobigeon, N.; Tourneret, J.; Albinet, M.; Goldstein, C.; and Karouche, N. IEEE Trans. Geoscience and Remote Sensing, 54(5): 2803–2811. May 2016.
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Hyperspectral unmixing with spectral variability using a perturbed linear mixing model. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Signal Processing, 64(2): 525–538. Feb. 2016.
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Linear and nonlinear unmixing in hyperspectral imaging. Dobigeon, N.; Altmann, Y.; Brun, N.; and Moussaoui, S. In Ruckebusch, C., editor(s), Resolving spectral mixtures – With application from ultrafast time-resolved spectroscopy to superresolution imaging, volume 30, of Data Handling in Science and Technology, pages 185–224. Elsevier, Oxford, U.K., 2016.
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Blind model-based fusion of multi-band and panchromatic images. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; Tourneret, J.; and Godsill, S. In IEEE Int. Conf. Multisensor Fusion Integration Intell. Syst. (MFI), pages 21–25, Baden-Baden, Germany, July 2016.
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Hyperspectral EELS image unmixing. Altmann, Y.; Brun, N.; Dobigeon, N.; March, K.; Moussaoui, S.; and Schneegans, O. In IASIM Conference in Spectral Imaging, Chamonix, France, July 2016.
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High-resolution Hyperspectral Image Fusion Based on Spectral Unmixing. Wei, Q.; Godsill, S.; Bioucas-Dias, J. M.; Dobigeon, N.; and Tourneret, J. In IEEE Int. Conf. Info. Fusion (FUSION), pages 1714–1719, Heidelberg, Germany, July 2016.
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Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach. Sodjo, J.; Giremus, A.; Caron, F.; Giovannelli, J.; and Dobigeon, N. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 1–5, Palma de Mallorca, Spain, June 2016.
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Democratic prior for anti-sparse coding. Elvira, C.; Chainais, P.; and Dobigeon, N. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 1–5, Palma de Mallorca, Spain, June 2016.
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Unmixing multitemporal hyperspectral images with variability: an online algorithm. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3351–3355, Shangai, China, March 2016.
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Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach – Complementary results. Ferraris, V.; Dobigeon, N.; Wei, Q.; and Chabert, M. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2016.
Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach – Complementary results [pdf]Paper   link   bibtex  
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation – Complementary results and supporting materials. Wei, Q.; Dobigeon, N.; Tourneret, J.; Bioucas-Dias, J. M.; and Godsill, S. Technical Report Department of Engineering, University of Cambridge, Cambridge, U.K., July 2016.
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Bayesian anti-sparse coding – Complementary results and supporting materials. Elvira, C.; Chainais, P.; and Dobigeon, N. Technical Report University of Lille, CNRS, CRIStAL and University of Toulouse, IRIT/INP-ENSEEIHT, France, July 2016.
Bayesian anti-sparse coding – Complementary results and supporting materials [pdf]Paper   link   bibtex  
  2015 (24)
Unsupervised unmixing of hyperspectral images accounting for endmember variability. Halimi, A.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 24(12): 4904-4917. Dec. 2015.
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Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization. Févotte, C.; and Dobigeon, N. IEEE Trans. Image Processing, 24(12): 4810–4819. Dec. 2015.
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Fast fusion of multi-band images based on solving a Sylvester equation. Wei, Q.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 24(11): 4109–4121. Nov. 2015.
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Hyperspectral pansharpening: a review. Loncan, L.; Almeida, L. B.; Bioucas-Dias, J. M.; Briottet, X.; Chanussot, J.; Dobigeon, N.; Fabre, S.; Liao, W.; Licciardi, G.; Simoes, M.; Tourneret, J.; Veganzones, M.; Vivone, G.; Wei, Q.; and Yokoya, N. IEEE Geoscience and Remote Sensing Magazine, 3(3): 27–46. Sept. 2015.
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Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation. Combrexelle, S.; Wendt, H.; Dobigeon, N.; Tourneret, J.; McLaughlin, S.; and Abry, P. IEEE Trans. Image Processing, 24(8): 2540–2551. Aug. 2015.
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Bayesian fusion of multi-band images. Wei, Q.; Dobigeon, N.; and Tourneret, J. IEEE J. Sel. Topics Signal Processing, 9(6): 1117–1127. Sept. 2015.
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Hyperspectral and multispectral image fusion based on a sparse representation. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Geoscience and Remote Sensing, 53(17): 3658–3668. Jul. 2015.
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Toward fast transform learning. Chabiron, O.; Malgouyres, F.; Tourneret, J.; and Dobigeon, N. Int. J. Comp. Vision, 114(2): 195–216. Sept. 2015.
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FUSE: a fast multi-band image fusion algorithm. Wei, Q.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Workshop Comput. Adv. in Multi-Sensor Adaptive Process. (CAMSAP), pages 161–164, Cancun, Mexico, Dec. 2015. Student paper contest finalist
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Unmixing multitemporal hyperspectral images accounting for endmember variability. Halimi, A.; Dobigeon, N.; Tourneret, J.; McLaughlin, S.; and Honeine, P. In Proc. European Signal Processing Conf. (EUSIPCO), pages 1686–1690, Nice, France, Sept. 2015.
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A perturbed linear mixing model accounting for spectral variability. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. In Proc. European Signal Processing Conf. (EUSIPCO), pages 819–823, Nice, France, Sept. 2015.
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Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method. Wei, Q.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, 2015.
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Hyperspectral unmixing accounting for spatial correlations and endmember variability. Halimi, A.; Dobigeon, N.; Tourneret, J.; and Honeine, P. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, 2015.
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Comparison of nine hyperspectral pansharpening methods. Loncan, L.; Bioucas-Dias, J. M.; Briottet, X.; Chanussot, J.; Dobigeon, N.; Fabre, S.; Liao, W.; Licciardi, G.; Simoes, M.; Tourneret, J.; Veganzones, M.; Vivone, G.; Wei, Q.; and Yokoya, N. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Milan, Italy, 2015.
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A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability. Halimi, A.; Dobigeon, N.; Tourneret, J.; and Honeine, P. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 2469–2473, Brisbane, Autralia, Apr. 2015.
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A Bayesian approach for the joint estimation of the multifractality parameter and integral scale based on the Whittle approximation. Combrexelle, S.; Wendt, H.; Abry, P.; Dobigeon, N.; Tourneret, J.; and McLaughlin, S. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3886–3890, Brisbane, Autralia, Apr. 2015.
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Estimation de variabilité pour le démélange non-supervisé d'images hyperspectrales. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. In Actes du XXVième Colloque GRETSI, Lyon, France, 2015. in french
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Estimation bayśienne locale du paramètre de multifractalité à l'aide d'un algorithme de Monte Carlo Hamiltonien. Combrexelle, S.; Wendt, H.; Tourneret, J.; Dobigeon, N.; McLaughlin, S.; and Abry, P. In Actes du XXVième Colloque GRETSI, Lyon, France, 2015. in french
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Online unmixing of multitemporal hyperspectral images accounting for spectral variability – Complementary results. Thouvenin, P.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2015.
Online unmixing of multitemporal hyperspectral images accounting for spectral variability – Complementary results [pdf]Paper   link   bibtex  
Detection and Correction of Glitches in a Multiplexed Multi-channel Data Stream – Application to the MADRAS Instrument – Complementary results and supporting materials. Wendt, H.; Dobigeon, N.; Tourneret, J.; Albinet, M.; Goldstein, C.; and Karouche, N. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2015.
Detection and Correction of Glitches in a Multiplexed Multi-channel Data Stream – Application to the MADRAS Instrument – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method – Complementary results and supporting materials. Wei, Q.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, March 2015.
Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Fast fusion of multi-band images based on solving a Sylvester equation – Complementary results and supporting materials. Wei, Q.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Feb. 2015.
Fast fusion of multi-band images based on solving a Sylvester equation – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Bayesian fusion of multi-band images – Complementary results and supporting materials. Wei, Q.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Feb. 2015.
Bayesian fusion of multi-band images – Complementary results and supporting materials [pdf]Paper   link   bibtex  
Fast spectral unmixing based on Dykstra's alternating projection. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; and Tourneret, J. 2015.
Fast spectral unmixing based on Dykstra's alternating projection [link]Paper   link   bibtex  
  2014 (21)
A comparison of nonlinear mixing models for vegetated areas using simulated and real hyperspectral data. Dobigeon, N.; Tits, L.; Somers, B.; Altmann, Y.; and Coppin, P. IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing, 7(6): 1869–1878. June 2014.
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Unsupervised post-nonlinear unmixing of hyperspectral images using a Hamiltonian Monte Carlo algorithm. Altmann, Y.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 23(6): 2663–2675. June 2014.
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Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. IEEE Trans. Image Processing, 23(5): 2148–2158. May 2014.
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Joint Bayesian estimation of close subspaces from noisy measurements. Besson, O.; Dobigeon, N.; and Tourneret, J. IEEE Signal Processing Letters, 21(2): 168–171. Feb. 2014.
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Nonlinear unmixing of hyperspectral images: Models and algorithms. Dobigeon, N.; Tourneret, J.; Richard, C.; Bermudez, J. C. M.; McLaughlin, S.; and Hero, A. O. IEEE Signal Processing Magazine, 31(1): 82–94. Jan. 2014.
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Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. IEEE Signal Processing Letters, 21(1): 47–50. Jan. 2014.
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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM. Park, S.; Dobigeon, N.; and Hero, A. O. Signal Processing, 94: 386–400. Jan. 2014.
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Inverse problem formulation for regularity estimation in images. Pustelnik, N.; Abry, P.; Wendt, H.; and Dobigeon, N. In Proc. IEEE Int. Conf. Image Processing (ICIP), pages 6081–6085, Paris, France, Oct. 2014. Top 10% Paper Award
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Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response. Wei, Q.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Image Processing (ICIP), pages 698–702, Paris, France, Oct. 2014. Invited paper
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Hybrid Bayesian variational scheme to handle parameter selection in total variation signal denoising. Frecon, J.; Pustelnik, N.; Dobigeon, N.; Wendt, H.; and Abry, P. In Proc. European Signal Processing Conf. (EUSIPCO), pages 1716–1720, Lisbon, Portugal, Sept. 2014.
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Fusion of multispectral and hyperspectral images based on sparse representation. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; and Tourneret, J. In Proc. European Signal Processing Conf. (EUSIPCO), pages 1577–1581, Lisbon, Portugal, Sept. 2014. Invited paper
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Learning a fast transform with a dictionary. Chabiron, O.; Malgouyres, F.; Tourneret, J.; and Dobigeon, N. In Proc. Int. Traveling Workshop Interactions between Sparse models and Technology (iTWIST), Namur, Belgium, Aug. 2014.
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Sampling from a multivariate gaussian distribution truncated on a simplex: a review. Altmann, Y.; McLaughlin, S.; and Dobigeon, N. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 113–116, Gold Coast, Australia, July 2014. Invited paper
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Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral data. Dobigeon, N.; Tits, L.; Somers, B.; Altmann, Y.; and Coppin, P. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, June 2014. invited paper
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Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. Altmann, Y.; Dobigeon, N.; Tourneret, J.; and McLaughlin, S. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3166–3170, Florence, Italy, May 2014.
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Bayesian fusion of hyperspectral and multispectral images. Wei, Q.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3176–3180, Florence, Italy, May 2014.
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A hierarchical sparsity-smoothness Bayesian model for L0-L1-L2 regularization. Chaari, L.; Batatia, H.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 1901–1905, Florence, Italy, May 2014.
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Procédé de détection et/ou de correction automatique d'erreurs dans un flux de données multiplexées. Wendt, H.; Dobigeon, N.; and Tourneret, J. Dec. 2014.
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Unsupervised unmixing of hyperspectral images accounting for endmember variability - Complementary results and supporting materials. Halimi, A.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2014.
Unsupervised unmixing of hyperspectral images accounting for endmember variability - Complementary results and supporting materials [pdf]Paper   link   bibtex  
Hyperspectral and multispectral image fusion based on a sparse representation. Wei, Q.; Bioucas-Dias, J. M.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2014.
Hyperspectral and multispectral image fusion based on a sparse representation [pdf]Paper   link   bibtex  
Détection de mélanges non-linéaires dans les images hyperspectrales. Dobigeon, N.; and Tourneret, J. Technical Report R-S13/OT-0004-071, Toulouse, France, Jan. 2014.
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  2013 (17)
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. IEEE Trans. Image Processing, 22(6): 2385–2397. June 2013.
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Unsupervised Bayesian linear unmixing of gene expression microarrays. Bazot, C.; Dobigeon, N.; Tourneret, J.; Zaas, A. K.; Ginsburg, G. S.; and Hero, A. O. BMC Bioinformatics, 14(99). March 2013.
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Nonlinear spectral unmixing of hyperspectral images using Gaussian processes. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. IEEE Trans. Signal Processing, 61(10): 2442–2453. May 2013.
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Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model. Altmann, Y.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 22(4): 1267–1276. Apr. 2013.
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Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image. Eches, O.; Benediktsson, J. A.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 22(1): 5–16. Jan. 2013.
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MCMC algorithms for supervised and unsupervised linear unmixing of hyperspectral images. Dobigeon, N.; Moussaoui, S.; Coulon, M.; Tourneret, J.; and Hero, A. O. In Mary, D.; Theys, C.; and Aime, C., editor(s), New Concepts in Imaging: Optical and Statistical Models, volume 59, of EAS Publications Series, pages 381–401. EDP Sciences, Les Ulis, France, 2013.
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Bayesian unsupervised unmixing of hyperspectral images using a post-nonlinear model. Altmann, Y.; Dobigeon, N.; and Tourneret, J. In Proc. European Signal Processing Conf. (EUSIPCO), pages 1–5, Marrakech, Morrocco, Sept. 2013.
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Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images. Dobigeon, N.; and Févotte, C. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, June 2013.
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Nonlinear hyperspectral unmixing using Gaussian processes. Altmann, Y.; Dobigeon, N.; Tourneret, J.; and McLaughlin, S. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, June 2013. Invited paper, Best Paper Award
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A robust test for nonlinear mixture detection in hyperspectral images. Altmann, Y.; Dobigeon, N.; Tourneret, J.; and Bermudez, J. C. M. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 2149–2153, Vancouver, Canada, June 2013.
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Bayesian estimation for the multifractality parameter. Wendt, H.; Dobigeon, N.; Tourneret, J.; and Abry, P. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 6556–6560, Vancouver, Canada, June 2013.
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Estimation bayésienne du paramètre de multifractalité. Wendt, H.; Dobigeon, N.; Tourneret, J.; and Abry, P. In Actes du XXIVième Colloque GRETSI, Brest, France, Sept. 2013. in french
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Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Nov. 2013.
Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection [pdf]Paper   link   bibtex  
Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm. Altmann, Y.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2013.
Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm [pdf]Paper   link   bibtex  
Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model. Altmann, Y.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, March 2013.
Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model [pdf]Paper   link   bibtex  
Apprentissage de dictionnaire pour la représentation parcimonieuse d'images de télédétection. Dobigeon, N.; and Tourneret, J. Technical Report R-S11/OT-0004-057, Toulouse, France, May 2013.
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A normalized scaled gradient method to solve non-negativity and equality constrained linear inverse problem – Application to spectral mixture analysis. Theys, C.; Lantéri, H.; Richard, C.; Dobigeon, N.; Tourneret, J.; and Ferrari, A. 2013.
A normalized scaled gradient method to solve non-negativity and equality constrained linear inverse problem – Application to spectral mixture analysis [link]Paper   link   bibtex  
  2012 (21)
Semi-blind sparse image reconstruction with application to MRFM. Park, S.; Dobigeon, N.; and Hero, A. O. IEEE Trans. Image Processing, 21(9): 3838–3849. Sept. 2012.
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Spectral mixture analysis of EELS spectrum-images. Dobigeon, N.; and Brun, N. Ultramicroscopy, 120: 25–34. Sept. 2012.
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CS decomposition based Bayesian subspace estimation. Besson, O.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Signal Processing, 60(8): 4210–4218. Aug. 2012.
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Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. IEEE Trans. Med. Imaging, 31(8): 1509–1520. Aug. 2012.
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Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method. Kail, G.; Tourneret, J.; Hlawatsch, F.; and Dobigeon, N. IEEE Trans. Signal Processing, 60(6): 2727–2743. June 2012.
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Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery. Altmann, Y.; Halimi, A.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Image Processing, 21(6): 3017–3025. June 2012.
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches. Bioucas-Dias, J. M.; Plaza, A.; Dobigeon, N.; Parente, M.; Du, Q.; Gader, P.; and Chanussot, J. IEEE J. Sel. Topics Appl. Earth Observations Remote Sensing, 5(2): 354–379. Apr. 2012.
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Hyperspectral image unmixing using a multiresolution sticky HDP. Mittelman, R.; Dobigeon, N.; and Hero, A. O. IEEE Trans. Signal Processing, 60(4): 1656–1671. Apr. 2012.
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Bayesian analysis of time-evolving gene expression data with hidden Markov model. Bazot, C.; Dobigeon, N.; Tourneret, J.; and Hero, A. O. In Proc. European Signal Processing Conf. (EUSIPCO), pages 944–948, Bucharest, Romania, Sept. 2012.
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Regularized Bayesian compressed sensing in ultrasound imaging. Dobigeon, N.; Basarab, A.; Tourneret, J.; and Kouamé, D. In Proc. European Signal Processing Conf. (EUSIPCO), pages 2600–2604, Bucharest, Romania, Sept. 2012.
link   bibtex  
Detecting nonlinear mixtures in hyperspectral images. Altmann, Y.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), Shangai, China, June 2012. invited paper
link   bibtex  
Bayesian subspace estimation using CS decomposition. Besson, O.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 2437–2440, Kyoto, Japan, March 2012.
link   bibtex  
Nonlinear unmixing of hyperspectral images using Gaussian processes. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 1249–1252, Kyoto, Japan, March 2012.
link   bibtex  
Constraining Martian mineralogical compositions using hyperspectral images. Schmidt, F.; Bourguignon, S.; Mouëlic, S. L.; Dobigeon, N.; and Tréguier, E. In Proc. Lunar and Planetary Science Conf. (LPSC), volume 43, The Woodlands, Texas, March 2012.
link   bibtex  
Variational semi-blind sparse image reconstruction with application to MRFM. Park, S.; Dobigeon, N.; and Hero, A. O. In Bouman, C. A.; Pollak, I.; and Wolfe, P. J., editor(s), Proc. SPIE-IS&T Electronic Imaging, Computational Imaging X, volume 8296, San Francisco, CA, Jan. 2012. SPIE
link   bibtex  
Un modèle Bayésien de mélange de lois Poisson-Gamma pour segmenter des images TEP. Irace, Z.; Pereyra, M.; Dobigeon, N.; and Batatia, H. In Actes de la Conférence Reconnaissance des Formes et Intelligence Artificielle (RFIA), Lyon, France, Jan. 2012. in french
link   bibtex  
Inférence bayésienne dans des problèmes inverses, myopes et aveugles en traitement du signal et des images. Dobigeon, N. October 2012.
link   bibtex  
Nonlinear spectral unmixing of hyperspectral images using Gaussian processes. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Jul. 2012.
Nonlinear spectral unmixing of hyperspectral images using Gaussian processes [pdf]Paper   link   bibtex  
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Apr. 2012.
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm [pdf]Paper   link   bibtex  
Algorithmes complémentaires de reconstruction de données de l'instrument MADRAS. Wendt, H.; Dobigeon, N.; and Tourneret, J. Technical Report Toulouse, France, Sept. 2012.
link   bibtex  
Algorithmes de reconstruction de données de l'instrument MADRAS. Wendt, H.; Dobigeon, N.; and Tourneret, J. Technical Report Toulouse, France, June 2012.
link   bibtex  
  2011 (37)
Minimum mean square distance estimation of a subspace. Besson, O.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Signal Processing, 59(12): 5709–5720. Dec. 2011.
link   bibtex  
Enhancing hyperspectral image unmixing with spatial correlations. Eches, O.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Geoscience and Remote Sensing, 49(11): 4239–4247. Nov. 2011.
link   bibtex  
Nonlinear unmixing of hyperspectral images using a generalized bilinear model. Halimi, A.; Altmann, Y.; Dobigeon, N.; and Tourneret, J. IEEE Trans. Geoscience and Remote Sensing, 49(11): 4153–4162. Nov. 2011.
link   bibtex  
Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection. Huang, Y.; Zaas, A. K.; Rao, A.; Dobigeon, N.; Woolf, P. J.; Veldman, T.; Oien, N. C.; McClain, M. T.; Varkey, J. B.; Nicholson, B.; Carin, L.; Kingsmore, S.; Woods, C. W.; Ginsburg, G. S.; and Hero, A. O. PLoS Genetics, 8(7): e1002234. Aug. 2011.
link   bibtex  
Bayesian compressed sensing in ultrasound imaging. Quinsac, C.; Dobigeon, N.; Basarab, A.; Tourneret, J.; and Kouamé, D. In Proc. IEEE Int. Workshop Comput. Adv. in Multi-Sensor Adaptive Process. (CAMSAP), pages 101–104, San Juan, Puerto Rico, Dec. 2011. invited paper
link   bibtex  
Bayesian estimation of a subspace. Besson, O.; Dobigeon, N.; and Tourneret, J. In Rec. 45th IEEE Asilomar Conf. Signals, Systems and Computers (Asilomar), pages 629–633, Pacific Grove, CA, Nov. 2011.
link   bibtex  
Accuracy and performance of linear unmixing techniques for detecting minerals on Omega/Mars Express. Schmidt, F.; Bourguignon, S.; Mouëlic, S. L.; Dobigeon, N.; Theys, C.; and Tréguier, E. In Proc. European Planetary Science Congress (EPSC), volume 6, Nantes, France, Oct. 2011.
link   bibtex  
Investigation of a small set of hyperspectral images through non-negative source separation. Tréguier, E.; Schmidt, F.; Erard, S.; Schmidt, A.; Cardesín, A.; Martin, P.; Pinet, P.; Moussaoui, S.; and Dobigeon, N. In Proc. European Planetary Science Congress (EPSC), volume 6, Nantes, France, Oct. 2011.
link   bibtex  
Segmentation of high frequency ultrasound images using a spatially coherent generalized Rayleigh mixture model. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. In Proc. European Signal Processing Conf. (EUSIPCO), pages 664–668, Barcelona, Spain, Sept. 2011. invited paper
link   bibtex  
Unmixing hyperspectral images using the generalized bilinear model. Halimi, A.; Altmann, Y.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pages 1886–1889, Vancouver, Canada, July 2011.
link   bibtex  
A polynomial post nonlinear model for hyperspectral image unmixing. Altmann, Y.; Halimi, A.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pages 1882–1885, Vancouver, Canada, July 2011.
link   bibtex  
Nonlinear unmixing of hyperspectral images using radial basis functions and orthogonal least squares. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. In Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pages 1151–1154, Vancouver, Canada, July 2011. invited paper
link   bibtex  
Bayesian segmentation of chest tumors in PET Scans using a Poisson-Gamma mixture model. Irace, Z.; Pereyra, M.; Dobigeon, N.; and Batatia, H. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 809–812, Nice, France, June 2011.
link   bibtex  
Nonlinear unmixing of hyperspectral images using a generalized bilinear model. Halimi, A.; Altmann, Y.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 413–416, Nice, France, June 2011.
link   bibtex  
Accuracy and performance of linear unmixing techniques for detecting minerals on Omega/Mars Express. Schmidt, F.; Bourguignon, S.; Mouëlic, S. L.; Dobigeon, N.; Theys, C.; and Tréguier, E. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Lisbon, Portugal, June 2011. invited paper
link   bibtex  
Joint spectral classification and unmixing using adaptative pixel neighborhoods. Eches, O.; Benediktsson, J. A.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Lisbon, Portugal, June 2011.
link   bibtex  
Bilinear models for nonlinear unmixing of hyperspectral images. Altmann, Y.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Lisbon, Portugal, June 2011.
link   bibtex  
Labeling skin tissues in ultrasound images using a generalized Rayleigh mixture model. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 729–732, Prague, Czech Republic, May 2011.
link   bibtex  
Variational methods for spectral unmixing of hyperspectral unmixing. Eches, O.; Dobigeon, N.; Tourneret, J.; and Snoussi, H. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 957–960, Prague, Czech Republic, May 2011.
link   bibtex  
Bernoulli-Gaussian model for gene expression analysis. Bazot, C.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 5996–5999, Prague, Czech Republic, May 2011. invited paper
link   bibtex  
Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery. Altmann, Y.; Halimi, A.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 1009–1012, Prague, Czech Republic, May 2011.
link   bibtex  
Non-negative Matrix Factorisation in Context. Schmidt, A.; Tréguier, E.; Schmidt, F.; Moussaoui, S.; and Dobigeon, N. In Proc. European Geosciences Union General Assembly (EGU), volume 11, Vienna, Austria, April 2011.
link   bibtex  
Analysis of a collection of planetary hyperspectral images through non-negative source separation. Tréguier, E.; Schmidt, A.; Schmidt, F.; Cardesín, A.; Erard, S.; Martin, P.; Pinet, P.; Moussaoui, S.; and Dobigeon, N. In Proc. European Geosciences Union General Assembly (EGU), volume 13, Vienna, Austria, April 2011.
link   bibtex  
Myopic reconstruction and its application to MRFM data. Park, S.; Dobigeon, N.; and Hero, A. O. In Bouman, C. A.; Pollak, I.; and Wolfe, P. J., editor(s), Proc. SPIE-IS&T Electronic Imaging, Computational Imaging IX, volume 7873, pages 787303-1–787303-14, San Francisco, CA, Jan. 2011. SPIE
link   bibtex  
Un modèle de mélange de lois Rayleigh généralisées pour la classification des échographies cutanées. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. In Actes du XXIIIième Colloque GRETSI, Bordeaux, France, Sept. 2011. in french
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Démélange non linéaire d'images hyperspectrales à l'aide de fonctions radiales de base et de moindres carrés orthogonaux. Altmann, Y.; Dobigeon, N.; McLaughlin, S.; and Tourneret, J. In Actes du XXIIIième Colloque GRETSI, Bordeaux, France, Sept. 2011. in french
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Méthodes variationnelles pour le démélange d'images hyperspectrales. Eches, O.; Dobigeon, N.; Tourneret, J.; and Snoussi, H. In Actes du XXIIIième Colloque GRETSI, Bordeaux, France, Sept. 2011. in french
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Echantillonnage compressé Bayésien en imagerie ultrasonore. Quinsac, C.; Dobigeon, N.; Basarab, A.; Tourneret, J.; and Kouamé, D. In Actes du XXIIIième Colloque GRETSI, Bordeaux, France, Sept. 2011. in french
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Modèle Bernoulli-Gaussien pour l'analyse génétique. Bazot, C.; Dobigeon, N.; Tourneret, J.; and Hero, A. O. In Actes du XXIIIième Colloque GRETSI, Bordeaux, France, Sept. 2011. in french
link   bibtex  
CS decomposition based Bayesian subspace estimation. Besson, O.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, ISAE & IRIT/INP-ENSEEIHT, France, Jul. 2011.
CS decomposition based Bayesian subspace estimation [pdf]Paper   link   bibtex  
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model. Pereyra, M.; Dobigeon, N.; Batatia, H.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2011.
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model [pdf]Paper   link   bibtex  
Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model. Altmann, Y.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Nov. 2011.
Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [pdf]Paper   link   bibtex  
Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery. Altmann, Y.; Halimi, A.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Nov. 2011.
Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery [pdf]Paper   link   bibtex  
Minimum mean square distance estimation of a subspace. Besson, O.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, ISAE & IRIT/INP-ENSEEIHT, France, Jul. 2011.
Minimum mean square distance estimation of a subspace [pdf]Paper   link   bibtex  
Variational methods for spectral unmixing of hyperspectral images. Eches, O.; Dobigeon, N.; Tourneret, J.; and Snoussi, H. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, May 2011.
Variational methods for spectral unmixing of hyperspectral images [pdf]Paper   link   bibtex   1 download  
Enhancing hyperspectral image unmixing with spatial correlations. Eches, O.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Jan. 2011.
Enhancing hyperspectral image unmixing with spatial correlations [pdf]Paper   link   bibtex  
Désentrelacement des mesures TDOA et FDOA. Tourneret, J.; Dobigeon, N.; and Ferrari, A. Technical Report R-S09/RE-0001-011, Toulouse, France, March 2011.
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  2010 (16)
Implementation strategies for hyperspectral unmixing using Bayesian source separation. Schmidt, F.; Guiheneuf, M.; Moussaoui, S.; Tréguier, E.; Schmidt, A.; and Dobigeon, N. IEEE Trans. Geoscience and Remote Sensing, 48(11): 4003–4013. Nov. 2010.
link   bibtex  
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm. Eches, O.; Dobigeon, N.; and Tourneret, J. IEEE J. Sel. Topics Signal Processing, 4(3): 582–591. June 2010.
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Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery. Eches, O.; Dobigeon, N.; Mailhes, C.; and Tourneret, J. IEEE Trans. Image Processing, 19(6): 1403–1413. June 2010.
link   bibtex  
Bayesian orthogonal component analysis for sparse representation. Dobigeon, N.; and Tourneret, J. IEEE Trans. Signal Processing, 58(5): 2675–2685. May 2010.
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Algorithmes bayésiens pour le démélange supervisé, semi-supervisé et non-supervisé images hyperspectrales. Dobigeon, N.; Moussaoui, S.; Coulon, M.; Tourneret, J.; and Hero, A. O. Traitement du signal, 27(1): 79–108. Aug. 2010. invited paper
Algorithmes bayésiens pour le démélange supervisé, semi-supervisé et non-supervisé images hyperspectrales [link]Paper   link   bibtex  
Investigating Martian and Venusian hyperspectral datasets through Positive Source Separation. Tréguier, E.; Schmidt, F.; Schmidt, A.; Moussaoui, S.; Dobigeon, N.; Erard, S.; Cardesín, A.; Pinet, P.; and Martin, P. In AGU Fall Meeting Abstracts, San Francisco, CA, Dec. 2010. abstract #P53B-1518
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Unsupervised Bayesian analysis of gene expression patterns. Bazot, C.; Dobigeon, N.; Tourneret, J.; and Hero, A. O. In Rec. 44th IEEE Asilomar Conf. Signals, Systems and Computers (Asilomar), pages 364–368, Pacific Grove, CA, Nov. 2010. invited paper
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Multi-Image Unsupervised Spectral Analysis. Schmidt, A.; Tréguier, E.; Schmidt, F.; Guiheneuf, M.; Moussaoui, S.; Dobigeon, N.; and Pelloquin, C. In Proc. European Planetary Science Congress (EPSC), volume 5, Roma, Italy, Sept. 2010.
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Unmixing hyperspectral images using Markov random fields. Eches, O.; Dobigeon, N.; and Tourneret, J. In Mohammad-Djafari, A.; Bercher, J.; and Bessiére, P., editor(s), Proc. Int. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt), volume 1305, of AIP Conf. Proc., pages 303-310, Chamonix, France, July 2010. AIP invited paper
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Accuracy and performance of optimized Bayesian source separation for hyperspectral unmixing. Schmidt, F.; Schmidt, A.; Tréguier, E.; Guiheneuf, M.; Moussaoui, S.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Reykjavík, Iceland, Jun. 2010.
link   bibtex  
Markov random fields for joint unmixing and segmentation of hyperspectral images. Eches, O.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Reykjavík, Iceland, Jun. 2010. invited paper
link   bibtex  
Matrix approximation techniques for unsupervised hyperspectral data analysis. Schmidt, A.; Schmidt, F.; Tréguier, E.; Moussaoui, S.; and Dobigeon, N. In Proc. European Geosciences Union General Assembly (EGU), volume 12, Vienna, Austria, May 2010.
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Multivariate approach for evaluating the composition of Meridiani spherules. Tréguier, E.; Schmidt, F.; Schmidt, A.; Guiheneuf, M.; Moussaoui, S.; Dobigeon, N.; and Martin, P. In Proc. European Geosciences Union General Assembly (EGU), volume 12, Vienna, Austria, May 2010.
link   bibtex  
A partially collapsed Gibbs sampler for parameters with local constraints. Kail, G.; Tourneret, J.; Hlawatsch, F.; and Dobigeon, N. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3886–3889, Dallas, USA, March 2010.
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A reversible-jump MCMC algorithm for estimating the number of endmembers in the normal compositional model. Application to the unmixing of hyperspectral images. Eches, O.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 1222–1225, Dallas, USA, March 2010.
link   bibtex  
Nonlinear unmixing of hyperspectral images using a generalized bilinear model. Halimi, A.; Altmann, Y.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2010.
Nonlinear unmixing of hyperspectral images using a generalized bilinear model [pdf]Paper   link   bibtex  
  2009 (20)
Bayesian separation of spectral sources under non-negativity and full additivity constraints. Dobigeon, N.; Moussaoui, S.; Tourneret, J.; and Carteret, C. Signal Processing, 89(12): 2657–2669. Dec. 2009.
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Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery. Dobigeon, N.; Moussaoui, S.; Coulon, M.; Tourneret, J.; and Hero, A. O. IEEE Trans. Signal Processing, 57(11): 4355–4368. Nov. 2009.
link   bibtex  
Hierarchical Bayesian sparse image reconstruction with application to MRFM. Dobigeon, N.; Hero, A. O.; and Tourneret, J. IEEE Trans. Image Processing, 18(9): 2059–2070. Sept. 2009.
link   bibtex  
Subspace-based Bayesian blind source separation for hyperspectral imagery. Dobigeon, N.; Moussaoui, S.; Coulon, M.; Tourneret, J.; and Hero, A. O. In Proc. IEEE Int. Workshop Comput. Adv. in Multi-Sensor Adaptive Process. (CAMSAP), volume 4, pages 372–375, Aruba, Dutch Antilles, Dec. 2009. invited paper
link   bibtex  
Material identification on hyperspectral images using Bayesian source separation. Schmidt, F.; Moussaoui, S.; and Dobigeon, N. In Proc. European Planetary Science Congress (EPSC), volume 4, Potsdam, Germany, Sept. 2009.
link   bibtex  
Multivariate approach for evaluating the composition of Meridiani spherules. Tréguier, E.; Schmidt, F.; Pinet, P.; Martin, P.; d'Uston , C.; Moussaoui, S.; and Dobigeon, N. In Proc. European Planetary Science Congress (EPSC), volume 4, Potsdam, Germany, Sept. 2009.
link   bibtex  
Linear unmixing of hyperspectral images using a scaled gradient method. Theys, C.; Dobigeon, N.; Tourneret, J.; and Lantéri, H. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 729–732, Cardiff, UK, Aug. 2009.
link   bibtex  
Unmixing hyperspectral images using a normal compositional model and MCMC methods. Eches, O.; Dobigeon, N.; Mailhes, C.; and Tourneret, J. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 646–649, Cardiff, UK, Aug. 2009.
link   bibtex  
Material identification on Martian hyperspectral images using Bayesian source separation. Schmidt, F.; Moussaoui, S.; and Dobigeon, N. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Grenoble, France, Aug. 2009.
link   bibtex  
NCM-based Bayesian algorithm for hyperspectral unmixing. Eches, O.; Dobigeon, N.; and Tourneret, J. In Proc. IEEE GRSS Workshop on Hyperspectral Image and SIgnal Processing: Evolution in Remote Sensing (WHISPERS), pages 1–4, Grenoble, France, Aug. 2009.
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Bayesian sparse image reconstruction for MRFM. Dobigeon, N.; Hero, A. O.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 2933–2936, Taipei, ROC, April 2009.
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Library-based linear unmixing for hyperspectral imagery via reversible jump MCMC sampling. Dobigeon, N.; and Tourneret, J. In Proc. IEEE/AIAA Aerospace Conference (AeroConf), volume 1–6, Big Sky, USA, March 2009. invited paper
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MCMC sampling for joint segmentation of wind speed and direction. Dobigeon, N.; and Tourneret, J. In Proc. IEEE Digital Signal Processing Workshop (DSP), pages 250–255, Marco Island, USA, Jan. 2009. invited paper
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Reconstruction bayésienne images MRFM parcimonieuses. Dobigeon, N.; Hero, A. O.; and Tourneret, J. In Actes du XXIIième Colloque GRETSI, Dijon, France, Sept. 2009. in french
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Extraction de composants purs et démélange linéaire bayésiens en imagerie hyperspectrale. Dobigeon, N.; Moussaoui, S.; Coulon, M.; and Tourneret, J. In Actes du XXIIième Colloque GRETSI, Dijon, France, Sept. 2009. in french
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Analyse d'images hyperspectrales à l'aide d'un modèle de mélange de spectres aléatoires. Eches, O.; Dobigeon, N.; and Tourneret, J. In Actes du XXIIième Colloque GRETSI, Dijon, France, Sept. 2009. in french
link   bibtex  
Bayesian orthogonal component analysis for sparse representation. Extension to non-homogeneous sparsity level over times. Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Nov. 2009.
Bayesian orthogonal component analysis for sparse representation. Extension to non-homogeneous sparsity level over times [pdf]Paper   link   bibtex  
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm. Eches, O.; Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Oct. 2009.
Estimating the number of endmembers in hyperspectral images using the normal compositional model and a hierarchical Bayesian algorithm [pdf]Paper   link   bibtex  
Unmixing hyperspectral images using a Normal Compositional Model and MCMC methods. Eches, O.; Dobigeon, N.; Mailhes, C.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Mar. 2009.
Unmixing hyperspectral images using a Normal Compositional Model and MCMC methods [pdf]Paper   link   bibtex  
Sparse reconstruction of molecular images: the MRFM challenge. Dobigeon, N.; Hero, A. O.; Tourneret, J.; and Rugar, D. 2009. submitted as a white paper to the Special Issue on Medical Imaging
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  2008 (4)
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery. Dobigeon, N.; Tourneret, J.; and Chang, C. IEEE Trans. Signal Processing, 56(7): 2684–2695. Jul. 2008.
link   bibtex  
Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix. Dobigeon, N.; Tourneret, J.; and Hero, A. O. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pages 3433–3436, Las Vegas, USA, March 2008.
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Bayesian sampling of structured noise covariance matrix for hyperspectral imagery. Dobigeon, N.; and Tourneret, J. Technical Report University of Toulouse, IRIT/INP-ENSEEIHT, France, Dec. 2008.
Bayesian sampling of structured noise covariance matrix for hyperspectral imagery [pdf]Paper   link   bibtex  
Recursive computation of the normalization constant of a multivariate Gaussian distribution truncated on a simplex. Dobigeon, N.; and Tourneret, J. Technical Report University of Michigan, USA, Jan. 2008.
Recursive computation of the normalization constant of a multivariate Gaussian distribution truncated on a simplex [pdf]Paper   link   bibtex  
  2007 (10)
Joint segmentation of wind speed and direction using a hierarchical model. Dobigeon, N.; and Tourneret, J. Computational Statistics and Data Analysis, 51(12): 5603–5621. Aug. 2007.
link   bibtex  
Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach. Dobigeon, N.; Tourneret, J.; and Davy, M. IEEE Trans. Signal Processing, 55(4): 1251–1263. April 2007.
link   bibtex  
Joint Segmentation of Multivariate Astronomical Time Series: Bayesian Sampling with a Hierarchical Model. Dobigeon, N.; Tourneret, J.; and Scargle, J. D. IEEE Trans. Signal Processing, 55(2): 414–423. Feb. 2007.
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Blind unmixing of linear mixtures using a hierarchical Bayesian model. Application to spectroscopic signal analysis. Dobigeon, N.; Tourneret, J.; and Moussaoui, S. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 79–83, Madison, USA, Aug. 2007.
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Spectral unmixing of hyperspectral images using a hierarchical Bayesian model. Dobigeon, N.; and Tourneret, J. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), volume 3, pages 1209–1212, Honolulu, Hawaii, USA, April 2007. Student paper contest finalist
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Séparation Bayésienne de sources spectrales sous contraintes de positivité et d'additivité. Dobigeon, N.; Moussaoui, S.; and Tourneret, J. In Actes du XXIième Colloque GRETSI, Troyes, France, Sept. 2007. in french
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Modèles bayésiens hiérarchiques pour le traitement multi-capteur. Dobigeon, N. Ph.D. Thesis, Institut National Polytechnique de Toulouse, Toulouse, France, October 2007.
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Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery. Dobigeon, N.; Tourneret, J.; and Chang, C. Technical Report IRIT/ENSEEIHT/TeSA & University of Maryland, France and USA, Mar. 2007.
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery [pdf]Paper   link   bibtex  
Efficient sampling according to a multivariate Gaussian distribution truncated on a simplex. Dobigeon, N.; and Tourneret, J. Technical Report IRIT/ENSEEIHT/TeSA, France, Mar. 2007.
Efficient sampling according to a multivariate Gaussian distribution truncated on a simplex [pdf]Paper   link   bibtex  
Truncated Multivariate Gaussian Distribution on a Simplex. Dobigeon, N.; and Tourneret, J. Technical Report IRIT/ENSEEIHT/TeSA, France, Jan. 2007.
Truncated Multivariate Gaussian Distribution on a Simplex [pdf]Paper   link   bibtex  
  2006 (3)
Joint segmentation of multivariate Poissonian time series Applications to Burst and Transient Source Experiments. Dobigeon, N.; Tourneret, J.; and Scargle, J. D. In Proc. European Signal Processing Conf. (EUSIPCO), Florence, Italy, Sept. 2006.
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Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach. Dobigeon, N.; Tourneret, J.; and Davy, M. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), volume 3, pages 1–4, Toulouse, France, May 2006.
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Joint segmentation of piecewise constant autoregressive processes processes by using a hierarchical model and a Bayesian sampling approach. Dobigeon, N.; Tourneret, J.; and Davy, M. Technical Report IRIT/ENSEEIHT/TeSA & LAGIS, France, Mar. 2006.
Joint segmentation of piecewise constant autoregressive processes processes by using a hierarchical model and a Bayesian sampling approach [pdf]Paper   link   bibtex  
  2005 (3)
Change-point detection in astronomical data by using a hierarchical model and a baysesian sampling approach. Dobigeon, N.; Tourneret, J.; and Scargle, J. D. In Proc. IEEE Workshop on Statistical Signal Processing (SSP), pages 369–374, Bordeaux, France, July 2005.
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Performance comparison of geometric and statistical methods for endmembers extraction in hyperspectral imagery. Dobigeon, N.; and Achard, V. In Bruzzone, L., editor(s), Proc. SPIE Image and Signal Processing for Remote Sensing XI, volume 5982, pages 598213-1–598213-10, Brugge, Belgium, Oct. 2005. SPIE
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Segmentation conjointe de données poissonniennes. Dobigeon, N.; and Tourneret, J. In Actes du XXième Colloque GRETSI, volume 1, pages 105–108, Louvain-la-Neuve, Belgium, Sept. 2005. in french
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  2004 (1)
Choix et implantation d'une méthode d'extraction de pôles de mélange dans une image hyperspectrale. Dobigeon, N. Master's thesis, Institut National Polytechnique de Toulouse, Toulouse, France, June 2004.
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  submitted (4)
On-the-fly spectral unmixing based on Kalman filtering. Kouakou, H.; de M. Goulard, J. H.; Vitale, R.; Oberlin, T.; Rousseau, D.; Ruckebusch, C.; and Dobigeon, N. . submitted.
On-the-fly spectral unmixing based on Kalman filtering [link]Paper   link   bibtex  
Efficient posterior sampling for Bayesian imaging with explicit score function-based priors. Faye, E. C.; Fall, M. D.; and Dobigeon, N. . submitted.
Efficient posterior sampling for Bayesian imaging with explicit score function-based priors [link]Paper   link   bibtex  
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling. Faye, E. C.; Fall, M. D.; and Dobigeon, N. . submitted.
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling [link]Paper   link   bibtex   1 download  
Bayesian nonparametric principal component analysis. Elvira, C.; Chainais, P.; and Dobigeon, N. . submitted.
Bayesian nonparametric principal component analysis [link]Paper   link   bibtex