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  2020 (15)
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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Asymptotically exact data augmentation: models, properties and algorithms. Vono, M.; Dobigeon, N.; and Chainais, P. Journal of Computational and Graphical Statistics, 00(0): 1–14. Nov. 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   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   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   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 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   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   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   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   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   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   bibtex  
  2016 (18)
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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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., 2016. to appear
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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   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   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   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   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   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   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   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   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   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   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   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   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   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   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   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.
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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
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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.
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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.
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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.
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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
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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
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Inférence bayésienne dans des problèmes inverses, myopes et aveugles en traitement du signal et des images. Dobigeon, N. October 2012.
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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   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   bibtex  
Algorithmes complémentaires de reconstruction de données de l'instrument MADRAS. Wendt, H.; Dobigeon, N.; and Tourneret, J. Technical Report Toulouse, France, Sept. 2012.
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Algorithmes de reconstruction de données de l'instrument MADRAS. Wendt, H.; Dobigeon, N.; and Tourneret, J. Technical Report Toulouse, France, June 2012.
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  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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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
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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.
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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.
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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
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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
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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
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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   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   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   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   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   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   bibtex  
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   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.
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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.
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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   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.
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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
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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.
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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.
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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   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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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)