| Time-frequency reassignment and synchrosqueezing: An overview F Auger, P Flandrin, YT Lin, S McLaughlin, S Meignen, T Oberlin, HT Wu IEEE Signal Processing Magazine 30 (6), 32-41, 2013 | 334 | 2013 |
| Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations T Oberlin, S Meignen, V Perrier IEEE Transactions on Signal Processing 63 (5), 1335-1344, 2015 | 181 | 2015 |
| A new algorithm for multicomponent signals analysis based on synchrosqueezing: With an application to signal sampling and denoising S Meignen, T Oberlin, S McLaughlin IEEE transactions on Signal Processing 60 (11), 5787-5798, 2012 | 143 | 2012 |
| The Fourier-based synchrosqueezing transform T Oberlin, S Meignen, V Perrier 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 107 | 2014 |
| Theoretical analysis of the second-order synchrosqueezing transform R Behera, S Meignen, T Oberlin Applied and Computational Harmonic Analysis 45 (2), 379-404, 2018 | 87 | 2018 |
| An alternative formulation for the empirical mode decomposition T Oberlin, S Meignen, V Perrier IEEE Transactions on Signal Processing 60 (5), 2236-2246, 2012 | 56 | 2012 |
| The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images M Clausel, T Oberlin, V Perrier Applied and Computational Harmonic Analysis 39 (3), 450-486, 2015 | 34 | 2015 |
| Adaptive multimode signal reconstruction from time–frequency representations S Meignen, T Oberlin, P Depalle, P Flandrin, S McLaughlin Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2016 | 33 | 2016 |
| Bayesian EEG source localization using a structured sparsity prior F Costa, H Batatia, T Oberlin, C d'Giano, JY Tourneret NeuroImage 144, 142-152, 2017 | 30 | 2017 |
| The second-order wavelet synchrosqueezing transform T Oberlin, S Meignen 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 28 | 2017 |
| The ASTRES toolbox for mode extraction of non-stationary multicomponent signals D Fourer, J Harmouche, J Schmitt, T Oberlin, S Meignen, F Auger, ... 2017 25th European Signal Processing Conference (EUSIPCO), 1130-1134, 2017 | 21 | 2017 |
| A novel time-frequency technique for multicomponent signal denoising T Oberlin, S Meignen, S McLaughlin 21st European Signal Processing Conference (EUSIPCO 2013), 1-5, 2013 | 15 | 2013 |
| On the mode synthesis in the synchrosqueezing method T Oberlin, S Meignen, V Perrier 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO …, 2012 | 14 | 2012 |
| Unmixing dynamic PET images with variable specific binding kinetics YC Cavalcanti, T Oberlin, N Dobigeon, S Stute, M Ribeiro, C Tauber Medical image analysis 49, 117-127, 2018 | 11 | 2018 |
| Factor analysis of dynamic PET images: beyond Gaussian noise YC Cavalcanti, T Oberlin, N Dobigeon, C Févotte, S Stute, MJ Ribeiro, ... IEEE transactions on medical imaging 38 (9), 2231-2241, 2019 | 10 | 2019 |
| Reconstruction of Partially Sampled Multiband Images—Application to STEM-EELS Imaging E Monier, T Oberlin, N Brun, M Tencé, M de Frutos, N Dobigeon IEEE Transactions on Computational Imaging 4 (4), 585-598, 2018 | 10 | 2018 |
| Multicomponent signal denoising with synchrosqueezing S Meignen, T Oberlin, S McLaughlin 2012 IEEE Statistical Signal Processing Workshop (SSP), 660-663, 2012 | 10 | 2012 |
| Synchrosqueezing transforms: From low-to high-frequency modulations and perspectives S Meignen, T Oberlin, DH Pham Comptes Rendus Physique 20 (5), 449-460, 2019 | 9 | 2019 |
| Recommendation from raw data with adaptive compound Poisson factorization O Gouvert, T Oberlin, C Févotte Uncertainty in Artificial Intelligence, 91-101, 2020 | 8 | 2020 |
| Time-frequency ridge analysis based on the reassignment vector S Meignen, T Gardner, T Oberlin 2015 23rd European Signal Processing Conference (EUSIPCO), 1486-1490, 2015 | 8 | 2015 |
sylvain meignenLaboratoire LJKVerified email at imag.fr
