Publications

Preprints

  1. Thouvenin, P.-A., Repetti, A., & Chainais, P. (2022). A distributed Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems.

Journal articles

  1. Thouvenin, P.-A., Abdulaziz, A., Dabbech, A., Repetti, A., & Wiaux, Y. (2022). Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations. Mon. Not. R. Astron. Soc.
  2. Thouvenin, P.-A., Dabbech, A., Jiang, M., Abdulaziz, A., Thiran, J.-P., Jackson, A., & Wiaux, Y. (2022). Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept. Mon. Not. R. Astron. Soc.
  3. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2019). Partially asynchronous distributed unmixing of hyperspectral images. IEEE Trans. Geosci. Remote Sens., 57(4), 2009–2021.
  4. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2018). A hierarchical Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images. IEEE Trans. Comput. Imag., 4(1), 32–45.
  5. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2016). Hyperspectral Unmixing with Spectral Variability Using a Perturbed Linear Mixing Model. IEEE Trans. Signal Process., 64(2), 525–538.
  6. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2016). Online unmixing of multitemporal hyperspectral images accounting for spectral variability. IEEE Trans. Image Process., 25(9), 3979–3990.

International conferences

  1. Thouvenin, P.-A., Repetti, A., & Chainais, P. (2022). A versatile distributed MCMC algorithm for large scale inverse problems. Proc. European Signal Process. Conf. (EUSIPCO), 2016–2020.
  2. Palud, P., Chainais, P., Le Petit, F., Bron, E., Thouvenin, P.-A., Vono, M., Einig, L., Santa-Maria, M. G., Gaudel, M., Orkisz, J. H., de Souza Magalhaes, V., Bardeau, S., Gerin, M., Goicoechea, J. R., Gratier, P., Guzman, V. V., Kainulainen, J., Levrier, F., Peretto, N., … Sievers, A. (2022). Mixture of noises and sampling of non-log-concave posterior distributions. Proc. European Signal Process. Conf. (EUSIPCO), 2031–2035.
  3. Thouvenin, P.-A., Abdulaziz, A., Jiang, M., Repetti, A., Dabbech, A., & Wiaux, Y. (2019, December). A Faceted Prior fo Scalable Wideband Imaging: Application to Radio Astronomy. Proc. IEEE Int. Workshop Comput. Adv. Multi-Sensor Adaptive Process. (CAMSAP).
  4. Thouvenin, P.-A., Abdulaziz, A., Jiang, M., Repetti, A., & Wiaux, Y. (2019). A Faceted Prior for Scalable Wideband Computational Imaging. Spars, 1–5.
  5. Thouvenin, P.-A., Repetti, A., Dabbech, A., & Wiaux, Y. (2018). Time-Regularized Blind Deconvolution Approach for Radio Interferometry. Proc. IEEE Sensor Array and Multichannel Signal Process. Workshop (SAM), 475–479.
  6. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2017). Unmixing Multitemporal Hyperspectral Images Accounting for Smooth and Abrupt Variations. Proc. European Signal Process. Conf. (EUSIPCO), 2378–2383.
  7. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2016). Unmixing multitemporal hyperspectral images with variability: an online algorithm. Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), 3351–3355.
  8. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2015). A Perturbed Linear Mixing Model Accounting for Spectral Variability. Proc. European Signal Process. Conf. (EUSIPCO), 819–823.

Workshop and conference abstracts

  1. Palud, P., Le Petit, F., Chainais, P., Bron, E., & Thouvenin, P.-A. (2022). Dealing with uncertainty in millimeter astronomy: towards more realistic noise model and Bayesian approach. Multi-Line Diagnostics of the Interstellar Medium.
  2. Palud, P., Bron, E., Thouvenin, P.-A., Le Petit, F., & Chainais, P. (2022). Sampler algorithm for non-convex inverse problem. 3rd IMA Conference on Inverse Problems from Theory to Application, 44.
  3. Thouvenin, P.-A., Repetti, A., & Chainais, P. (2022). Distributed sampling for imaging inverse problems in high dimension. 3rd IMA Conference on Inverse Problems from Theory to Application, 45.
  4. Palud, P., Le Petit, F., Chainais, P., Thouvenin, P.-A., Bron, E., & the ORION B consortium. (2022). Inferring physical conditions in star forming regions with new Bayesian approach and spatial regularization. Proceedings of the “Physique Et Chimie Du Milieu Interstellaire” (PCMI) Conference.
  5. Thouvenin, P.-A., Abdulaziz, A., Jiang, M., Dabbech, A., Repetti, A., Jackson, A., Thiran, J.-P., & Wiaux, Y. (2021). Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA). Proceedings of the XXXIVth URSI General Assembly (URSI GASS).

National conferences

  1. Palud, P., Chainais, P., Le Petit, F., Bron, E., Thouvenin, P.-A., Vono, M., Einig, L., Santa-Maria, M. G., Gaudel, M., Orkisz, J. H., de Souza Magalhaes, V., Bardeau, S., Gerin, M., Goicoechea, J. R., Gratier, P., Guzman, V. V., Kainulainen, J., Levrier, F., Peretto, N., … Sievers, A. (2022). Mélange de bruits et échantillonnage de posterior non log-concave. Actes Du XXIXième Colloque GRETSI, 705–708.
  2. Thouvenin, P.-A., Repetti, A., & Chainais, P. (2022). Un algorithme MCMC distribué pour la résolution de problèmes inverses de grande dimension. Actes Du XXIXième Colloque GRETSI, 697–700.
  3. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2017). Une approche distribuée asynchrone pour la factorisation en matrices non-négatives – application au démélange hyperspectral. Actes Du XXVIième Colloque GRETSI, 1065–1068.
  4. Thouvenin, P.-A., Dobigeon, N., & Tourneret, J.-Y. (2015). Estimation de variabilité pour le démélange non-supervisé d’images hyperspectrales. Actes Du XXVième Colloque GRETSI, 601–604.

Thesis

  1. Thouvenin, P.-A. (2017). Modeling spectral and temporal variabilities in hyperspectral image unmixing [Institut national polytechnique de Toulouse (INPT)].