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  2024 (8)
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  
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors. Wang, J.; Dobigeon, N.; Chabert, M.; Wang, D.; Huang, T.; and Huang, J. Information Fusion, 107. July 2024.
CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors [link]Paper   link   bibtex  
Superpixels meet essential spectra for fast Raman hyperspectral microimaging. Gilet, V.; Mabilleau, G.; Loumaigne, M.; Coic, L.; Vitale, R.; Oberlin, T.; de M. Goulart, J. H.; Dobigeon, N.; Ruckebusch, C.; and Rousseau, D. Optics Express, 32(1): 932–948. 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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A deep neural network to restore pulsar dynamic spectra corrupted by radio frequency interferences. Zhang, X.; Cognard, I.; and Dobigeon, N. In Proc. SPIE Astronomical Telescopes + Instrumentation, Yokohama, Japan, June 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  
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 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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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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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. Industrial reports
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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. Industrial reports
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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 hyperspec