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Preprints
Bayesian nonparametric principal component analysis, ,
C. Elvira, P. Chainais, and N. Dobigeon, preprint arXiv, , 2017.
Normalizing flow sampling with Langevin dynamics in the latent space, ,
F. Coeurdoux, N. Dobigeon, P. Chainais, .
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference, ,
F. Coeurdoux, N. Dobigeon, P. Chainais, .

Publications

          2023
A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems
Journal of Computational and Graphical Statistics, P.-A. Thouvenin, A. Repetti, P. Chainais, 2023, pp.1-35. ⟨10.1080/10618600.2023.2282501⟩
Quaternions in signal and image processing: A comprehensive and objective overview, ,
S. Miron, J. Flamant, N. L. Bihan, P. Chainais, and D. Brie,, IEEE Signal Processing Magazine, vol. 40, no. 6, pp. 26–40, 2023.
Efficient sampling of non log-concave posterior distributions with mixture of noises, ,
P. Palud, P.-A. Thouvenin, P. Chainais, E. Bron, and F. Le Petit, IEEE Transactions on Signal Processing, vol. 71, pp. 2491–2501, 2023.
Neural network-based emulation of interstellar medium models
Astronomy and Astrophysics - A&A, P. Palud, L. Einig, F. Le Petit , E. Bron, P. Chainais et al., 2023, 678, pp.A198. ⟨10.1051/0004-6361/202347074⟩
Méthode MCMC plug-and-play avec a priori génératif profond
F. Coeurdoux, N. Dobigeon, P. Chainais, XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI 2023, Grenoble, France.
Réduction d’un modèle astrophysique par réseaux de neurones
P. Palud, L. Einig, F. Le Petit , E. Bron, P. Chainais et al., XXIXème Colloque Francophone de Traitement du Signal et des Images, GRETSI 2023, Grenoble, France
Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
Astronomy and Astrophysics - A&A, Lucas Einig, Jérôme Pety, Antoine Roueff et al., 2023, 677 (A158), ⟨10.1051/0004-6361/202346064⟩
HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30 m maps of the Orion B GMC
Astronomy and Astrophysics - A&A, 2023, 679, pp.A4. ⟨10.1051/0004-6361/202346598⟩
          2022
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm, ,
M. Vono, N. Dobigeon and P. Chainais, SIAM Review, vol. 64, no. 1, pp. 3–56, 2022.
Learning Optimal Transport Between two empirical distributions with Normalizing Flows, ,
F. Coeurdoux, N. Dobigeon, P. Chainais Proc. of ECML-PKDD, 2022.
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training, ,
F. Coeurdoux, N. Dobigeon, P. Chainais, Proc. of ESANN, 2022.
Improved hyperspectral super-resolution based on coupled Tucker approximation, ,
C. Prévost, P. Chainais, R. Boyer, Proc. of ICIP, 2022.
Mixture of noises and sampling of non-convex posterior distributions, ,
P. Palud, P. Chainais, et al. Proc. of EUSIPCO, 2022.
A versatile distributed MCMC algorithm for large scale inverse problems, ,
P.A. Thouvenin, A. Repetti, P. Chainais Proc. of EUSIPCO, 2022.
Un algorithme MCMC distribué pour la résolution de problèmes inverses de grande dimension, ,
P.A. Thouvenin, A. Repetti, P. Chainais Proc. of GRETSI, 2022.
Mélange de bruits et échantillonnage de posterior non log-concave, ,
P. Palud, P. Chainais et al. Proc. of GRETSI, 2022.
Approximation du transport optimal entre distributions empiriques par flux de normalisation, ,
F. Coeurdoux, N. Dobigeon, P. Chainais, Proc. of GRETSI, 2022.
A public benchmark for denoising and detection methods, ,
J.-M. Miramont, R. Bardenet, P. Chainais, F. Auger Proc. of GRETSI, 2022.
          2021
Asymptotically exact data augmentation: models, properties and algorithms, ,
M. Vono, P. Chainais, N. Dobigeon, Journal of Computational and Graphical Statistics, vol. 30, no. 2, pp. 335-348, 2021PDF.
Quantitative inference of the H2 column densities from 3 mm molecular emission: A case study towards Orion B, ,
P. Gratier et al., Astronomy & Astrophysics, vol. 645, no. A27, January 2021.
C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud, ,
E. Bron et al., Astronomy & Astrophysics, vol. 645, no. A26, January 2021.
Tracers of the ionization fraction in dense and translucent gas. I. Automated exploitation of massive astrochemical model grids, ,
E. Bron et al., Astronomy & Astrophysics, vol. 645, no. A28, January 2021.
          2020
A determinantal point process for column subset selection, ,
A. Belhadji, R. Bardenet, P. Chainais, Journal of Machine Learning Research, 21 (197), pp. 1-62, 2020.
Kernel interpolation with continuous volume sampling, ,
A. Belhadji, R. Bardenet, P. Chainais, arXiv , Int. Conf. on Machine Learning (ICML), Vienna 2020.
On the zeros of the spectrogram of white noise, ,
R. Bardenet, J. Flamant, P. Chainais, Applied and Computational Harmonic Analysis, Vol. 48, Issue 2, pp. 682-705, 2020.
          2019
Kernel quadrature with DPPs,
A. Belhadji, R. Bardenet, P. Chainais, Proceedings of NeurIPS, Vancouver, Canada,, Poster, 2019.

Split-and-augmented Gibbs sampler - Application to large-scale inference problems,
M. Vono, N. Dobigeon, and P. Chainais, IEEE Transactions on Signal Processing, source, vol. 66, no. 17, pp. 4541–4552, 2019.

Time-frequency analysis of bivariate signals,
J. Flamant, P. Chainais, N. Le Bihan, , available online 1 June 2017, Applied and Computational Harmonic Analysis, source, Volume 46, Issue 2, pp. 351-383, 2019.
preprint arXiv
A correspondence between zeros of time-frequency transforms and Gaussian analytic functions,
R. Bardenet, P. Chainais, J. Flamant, A. Hardy, SampTA, 2019.
Efficient sampling through variable splitting-inspired bayesian hierarchical models,
M. Vono, N. Dobigeon, P. Chainais, IEEE ICASSP, 2019.
Bayesian image restoration under Poisson noise and log-concave prior,
M. Vono, N. Dobigeon, P. Chainais, IEEE ICASSP, 2019.
Un modèle augmenté asymptotiquement exact pour la restauration bayésienne d'images dégradées par un bruit de Poisson,
M. Vono, N. Dobigeon, P. Chainais, Proceedings of GRETSI, Lille, 2019.
Modèles augmentés asymptotiquement exacts,
M. Vono, N. Dobigeon, P. Chainais, Proceedings of GRETSI, Lille, 2019.
          2018
A complete framework for linear filtering of bivariate signals,
J. Flamant, P. Chainais, N. Le Bihan, IEEE Transactions on Signal Processing, source, vol. 66, no. 17, pp. 4541–4552, 2018.

Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler,
M. Vono, N. Dobigeon, P. Chainais, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) , Aalborg, Denmark, 2018 - Finalist for the Best Student Paper Awards.
Non-parametric characterization of gravitational-wave polarization,
J. Flamant, P. Chainais, E. Chassande-Mottin, F. Feng, and N. Le Bihan, 26th European Signal Processing Conference (EUSIPCO) , Roma, Italy, 2018.
Linear filtering of bivariate signals using quaternions,
J. Flamant, P. Chainais, N. Le Bihan, IEEE Statistical Signal Processing Workshop (SSP), pp. 154–158 , Freiburg, Germany, 2018 - Best Student Paper Award.
          2017
Spectral analysis of bivariate stationary random signals,
J. Flamant, N. Le Bihan, P. Chainais, IEEE Transactions on Signal Processing, vol. 65, Issue 23, pp.6135-6145, 2017.
link to journal
Time-frequency analysis of bivariate signals,
J. Flamant, P. Chainais, N. Le Bihan, to appear in Applied and Computational Harmonic Analysis, in press, 2017.
preprint arXiv
Indian Buffet Process Dictionary Learning : algorithms and applications to image processing,
H. P. Dang and P. Chainais, International Journal of Approximate Reasoning, vol. 83, pp 1-20, 2017.
link to journal
Bayesian anti-sparse coding,
C. Elvira, P. Chainais, N. Dobigeon, IEEE Transactions on Signal Processing , vol. 65, no 7, pp. 1660–1672, DOI: 10.1109/TSP.2016.2645543, 2017. Supplementary material.
link to journal
Bayesian non parametric subspace estimation,
C. Elvira, P. Chainais, N. Dobigeon, IEEE ICASSP, link to IEEEXplore 2017.
Polarization spectrogram of bivariate signals,
J. Flamant, P. Chainais, N. Le Bihan, IEEE ICASSP, link to IEEEXplore, 2017.
          2016
Towards dictionaries of optimal size: A bayesian non parametric approach,,
H. P. Dang and P. Chainais, Journal of Signal Processing Systems , pp. 1–12, 2016. DOI: 10.1007/s11265-016-1154-1.
Statistical performance analysis of a fast super-resolution technique using noisy translations,
P. Chainais, A. Leray, IEEE Transactions on Image Processing vol. 25, pp. 1699–1712, April 2016.
Matlab codes for illustrations.
Indian Buffet Process dictionary learning for image inpainting ,
H.P. Dang, P. Chainais, IEEE Workshop on Statistical Signal Processing, SSP 2016.
Democratic prior for anti-sparse coding ,
C. Elvira, P. Chainais, N. Dobigeon, IEEE Workshop on Statistical Signal Processing, SSP 2016.
          2015
A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements,
L.V. Nguyen, J.P. Laval, P. Chainais, Journal of Statistical Physics: Theory and Experiments, http://dx.doi.org/10.1088/1742-5468/2015/10/P10008, 2015.
Ultrasonic Tomography of Nonmixing Fluid Flows ,
Yu. V. Pyl'nov, L. M. Krutyansky, Yu. I. Kutlubaeva F. Zoueshtiagh, P. Chainais, V. Herman, P. Pernod Physics of Wave Phenomena, Vol. 23, No. 4, pp. 273-278, 2015.
"Dictionary Learning for a Sparse Appearance Model in Visual Tracking
S. Rousseau, C. Garnier, P. Chainais, IEEE ICIP 2015.
A page with some illustrations.
Apprentissage de dictionnaire pour un modèle d'apparence parcimonieux en suivi visuel,
S. Rousseau, C. Garnier, P. Chainais, GRETSI 2015.
A bayesian non parametric approach to learn dictionaries with adapted numbers of atoms,
H.P. Dang, P. Chainais, IEEE MLSP 2015, Best Paper Award.
"Approche bayésienne non paramétrique dans l'apprentissage du dictionnaire pour adapter le nombre d'atomes,
H.P. Dang, P. Chainais, GRETSI 2015.
Space-‐time reconstruction of finely resolved velocities of turbulent flows from low resolution measurements,
L.V. Nguyen, P. Chainais, J.P. Laval, 15th European Turbulence conference, Delft, Aug. 2015 (abstract)
          2014
Quantitative control of the error bounds of a fast super-resolution technique for microscopy and astronomy,
P. Chainais, P. Pfennig, A. Leray Proc. of ICASSP, 2014.
Synthèse en espace et temps du rayonnement acoustique d'une paroi sous excitation turbulente par synthèse spectrale 2D+T et formulation vibro-acoustique directe
M. Pachebat, N. Totaro, P. Chainais, O. Collery Congrès Français d'acoustique 2014, Poitiers, France, 6 p., p1921, papier N183, Apr 2014.
Example of a simulated Corcos pressure field.
          2013
Learning a common dictionary over a sensor network,
P. Chainais, C. Richard, Proc. of CAMSAP, 2013.
Quantification adaptative pour la stéganalyse d'images texturées,
P. Bas, P. Chainais, E. Zidel-Cauffet, Proc. of GRETSI, 2013.
Distributed dictionary learning over a sensor network,
P. Chainais, C. Richard, Proc. of CaP, 2013.
          2012
Towards dictionary learning from images with non gaussian noise,
P. Chainais, Proc. of Machine Learning and Signal Processing (MLSP), Santander, 2012.
          2011
Aligned carbon nanotubes based ultrasonic microtransducers for durability monitoring in civil engineering,
B. Lebental, P. Chainais, P. Chenevier, N. Chevalier, E. Delevoye, J.-M. Fabbri, S. Nicoletti, P. Renaux, A. Ghis
Nanotechnology, Vol. 22, 395501, 2011.
Scale invariant images in astronomy through the lens of multifractal modeling,
P. Chainais, V. Delouille, J.-F. Hochedez Proc. of ICIP, 2011.
Synthèse de textures multifractales directement sur des surfaces 3D ,
P. Chainais, M. Chevadonne, J.M. Favreau Proc. of GRETSI, 2011.
Caractérisation statistique d'une assemblée de nanotubes en imagerie microscopique,
P. Chainais, B. Lebental Proc. of GRETSI, 2011.
Virtual super resolution of textured images using multifractal stochastic processes ,
P. Chainais, E. Kœnig, V. Delouille, J.-F. Hochedez, Journal of Mathematical Imaging and Vision, Vol. 39, Nr. 1, pp. 28-44, 2011.
Supplemental material:
A page with some illustrations.

quietSun_movie.mov
Several realizations for the same original image: 1 2 3 4
          Before 2011
Multifractal random walks as fractional Wiener integrals ,
P. Abry, P. Chainais, L. Coutin, V. Pipiras IEEE Trans. on Information Theory, Vol. 55 no 8, pp.3825-3846, 2009.
Virtual resolution enhancement of scale invariant textured images using stochastic processes ,
E. Kœnig, P. Chainais, Proc. of ICIP 2009, Cairo, 2009.
Amélioration virtuelle de la résolution d'images du Soleil par augmentation d'information invariante d'échelle ,
E. Kœnig, P. Chainais, Proc. of GRETSI, Dijon, 2009.
Simulation de champs de pression turbulente en paroi par des processus aléatoires,
P. Chainais, M. Pachebat, Proc. of GRETSI, Dijon, 2009.
Example of a simulated Corcos pressure field.

Processus aléatoires invariants d'échelle et analyse multirésolution pour la modélisation d'observations de systèmes physiques,
P. Chainais, Habilitation à Diriger des Recherches, Université Blaise Pascal, sept. 2009.
3D reconstruction from SECCHI-EUVI images using an optical-flow algorithm: method description and observation of an erupting filament,
S. Gissot, J.-F. Hochedez, P. Chainais, J.-P. Antoine, Solar Physics, Vol. 252, Issue 2, pp.397-408, 2008.
Quantifying and containing the curse of high resolution coronal imaging ,
V. Delouille, P. Chainais, J.-F. Hochedez, Annales Geophysicae, Vol. 26 no 10, pp.3169-3184, 2008.
Multifractal analysis on the sphere,
E. Kœnig, P. Chainais, Proceedings of ICISP'08, Cherbourg, 2008.
Combining learning methods and time-scale analysis for defect diagnosis of a tramway guiding system,
Z. Hamou Mamar, P. Chainais, A. Aussem, IEEE Proceedings of MED'08, Ajaccio, 2008.
Spatial and temporal noise in solar EUV observations,
V. Delouille, P. Chainais, J.-F. Hochedez, Solar Physics, Vol. 248, no 2, Topical Issue on Solar Imaging, 2008.
Encadrement de la thèse de Z. Hamou Mamar : Analyse temps-échelle et reconnaissance des formes pour le diagnostic du système de guidage d'un tramway sur pneumatiques, Thèse de l'Université Blaise Pascal, soutenue e 18 juillet 2008.
Infinitely divisible cascades to model the statistics of natural images,
P. Chainais, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 29 no 12, Dec. 2007.
On causal stochastic equations for log-stable multiplicative cascades,
F.G. Schmitt, P. Chainais, Eur. Phys. J. B, Vol. 58, pp. 149-158, 2007.
Modeling images of the Quiet Sun in the extreme ultraviolet,
P. Chainais, V. Delouille, J.-F. Hochedez Proceedings of SPIE Wavelet XII,(15 p.) San Diego, 2007.
Intégrales stochastiques et cascades multiplicatives log-stables ,
P. Chainais, F. Schmitt dans Actes du GRETSI, Troyes, 2007.
Modélisation des images de Soleil calme dans l'extrême ultra-violet ,
P. Chainais, V. Delouille, J.-F. Hochedez dans Actes du GRETSI, Troyes, 2007.

          Before 2007

    International Journals

Multidimensional infinitely divisible cascades. Application to the modelling of intermittency in turbulence.,
P. Chainais, European Physical Journal B, Vol. 51, no. 2, pp. 229-243, 2006.

On non scale invariant infinitely divisible cascades,
P. Chainais, R. Riedi, et P. Abry, IEEE Trans. on Info. Theory, vol. 51, no. 3, March 2005.

New insights into the estimation of scaling exponents,
B. Lashermes, P. Abry, P. Chainais, Int. J. of Wavelets, Multiresolution and Information Processing, Vol. 2, no 4, pp. 497-523, Dec. 2004.

Intermittency and coherent structures in a swirling flow: a wavelet based analysis of joint pressure and velocity measurements,
P. Chainais, P. Abry et J.-F. Pinton, Phys. Fluid, vol 11, novembre 1999.

    French Journals

Warped infinitely divisible cascades: beyond power laws,
P. Chainais, R. Riedi, et P. Abry, Traitement du Signal, vol. 22, no. 1, 2005.

   International Conferences

Switching dynamic segmentation using a hidden Markov model of prediction experts operating on distinct wavelet scales,
A. Aussem, P. Chainais Proceedings of European Symposium on Artificial Neural Networks ESANN'2006.

Segmentation of EIT Images using a fuzzy clustering algorithm: a preliminary study,
V. Barra, V. Delouille, J.F. Hochedez, P. Chainais, Proceedings of European SPM-11, Leuven, sept. 2005.

Multi-dimensional infinitely divisible cascades to model the statistics of natural images,
P. Chainais, IEEE Proc. of ICIP'2005 .

Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system,
Z. Hamou-Mamar, P. Chainais, A. Aussem, Proceedings of European Symposium on Artificial Neural Networks ESANN'2006.

Scaling exponents estimation of multiscaling processes,
B. Lashermes, P. Abry, P. Chainais, Proc. of the Int. Conf. on Acoust. Speech and Sig. Proc., ICASSP Montreal 2004.

Remote continuous cardiac arrhythmias detection and monitoring,
H. Zhou, K. M. Hou, J. Ponsonnaille, L. Gineste, J. Coudon, G. de Sousa, C. de Vaulx, J.-J. Li, P. Chainais, R. Aufrère, A. Amamra and J.-P. Chanet, 2nd Internationnal Conference on E-he@lth in Common Europe. Krakow, 11-12 march 2004.

Limitation of scaling exponent estimation in turbulence
B. Lashermes, C. Baudet, P. Abry, et P. Chainais, Advances in Turbulence X, ETC10, Kluwer, 2004.

New insights on the estimation of scaling exponents,
B.Lashermes, P. Abry et P. Chainais, Wavelet and Statistics Conference, , Villard de Lans, France, sept. 2003.

Multifractal analysis and alpha-stable processes: a methodological contribution, P. Chainais, P. Abry, D. Veitch. In Proc. of the Int. Conf. on Acoust. Speech and Sig. Proc., ICASSP Istanbul, 2000.

Infinitely divisible cascade analysis of network traffic data, , D. Veitch, P. Abry, P. Flandrin, et P. Chainais. In Proc. of the Int. Conf. on Acoust. Speech and Sig. Proc., ICASSP Istanbul, 2000.

Remarkable features of multiplier distributions in turbulence, P. Chainais, E. Lévêque et P. Abry , Advances in Turbulence VIII, ETC8, Kluwer, 2000.

    National Conferences

Synthèse de champs scalaires multifractals : application à la synthèse de texture,
P. Chainais, J.J. Li, Proc. of GRETSI'2005 .

On non scale invariant infinitely divisible cascades,
P. Chainais, R. Riedi, et P. Abry, dans Actes du GRETSI, Paris, 2003.

De l'estimation des exposants des lois d'échelle ,
B. Lashermes, P. Abry, et P. Chainais, dans Actes du GRETSI, Paris, 2003.

Scale invariant infinitely divisible cascades,
P. Chainais, R. Riedi, et P. Abry, dans Proceedings of PSIP'03, Grenoble, 2003.

Compound Poisson Cascades,
P. Chainais, R. Riedi, et P. Abry, Annales de l'Université Blaise Pascal
Colloque Auto-Similarité et Applications, Clermont-Ferrand 2002.
Article
comp_Poiss_casc.m (MATLAB function).
Transparents.

Analyse et modélisation de séries temporelles à l'aide de cascades. Application à l'étude du trafic internet,
P. Chainais, S. Roux, et P. Abry, Actes du GRETSI, Toulouse 2001.

    Miscellaneous

Étude des structures de basse pression dans un écoulement turbulent , P. Chainais, Rapport de Stage de DEA, juillet 1997.

Accueil   | Publications   | Exposés   | Software   | CV   | Collaborateurs   | Liens   | Thèse   | Contact