| 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 | 604 | 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 | 407 | 2015 |
| The Fourier-based synchrosqueezing transform T Oberlin, S Meignen, V Perrier 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 309 | 2014 |
| 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 | 206 | 2012 |
| Theoretical analysis of the second-order synchrosqueezing transform R Behera, S Meignen, T Oberlin Applied and Computational Harmonic Analysis 45 (2), 379-404, 2018 | 193 | 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 | 79 | 2012 |
| The second-order wavelet synchrosqueezing transform T Oberlin, S Meignen 2017 IEEE international conference on acoustics, speech and signal …, 2017 | 64 | 2017 |
| 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 | 62 | 2016 |
| Synchrosqueezing transforms: From low-to high-frequency modulations and perspectives S Meignen, T Oberlin, DH Pham Comptes Rendus Physique 20 (5), 449-460, 2019 | 58 | 2019 |
| 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 | 49 | 2017 |
| 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 | 46 | 2015 |
| Reduced-complexity end-to-end variational autoencoder for on board satellite image compression V Alves de Oliveira, M Chabert, T Oberlin, C Poulliat, M Bruno, C Latry, ... Remote Sensing 13 (3), 447, 2021 | 42 | 2021 |
| 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 | 38 | 2017 |
| Satellite image compression and denoising with neural networks VA de Oliveira, M Chabert, T Oberlin, C Poulliat, M Bruno, C Latry, ... IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 35 | 2022 |
| Hyperspectral and multispectral image fusion under spectrally varying spatial blurs–Application to high dimensional infrared astronomical imaging C Guilloteau, T Oberlin, O Berné, N Dobigeon IEEE Transactions on Computational Imaging 6, 1362-1374, 2020 | 27 | 2020 |
| Darod: A deep automotive radar object detector on range-doppler maps C Decourt, R VanRullen, D Salle, T Oberlin 2022 IEEE Intelligent Vehicles Symposium (IV), 112-118, 2022 | 22 | 2022 |
| Ordinal non-negative matrix factorization for recommendation O Gouvert, T Oberlin, C Févotte International Conference on Machine Learning, 3680-3689, 2020 | 22 | 2020 |
| Phase retrieval with Bregman divergences and application to audio signal recovery PH Vial, P Magron, T Oberlin, C Févotte IEEE Journal of Selected Topics in Signal Processing 15 (1), 51-64, 2021 | 21 | 2021 |
| Simulated JWST data sets for multispectral and hyperspectral image fusion C Guilloteau, T Oberlin, O Berné, É Habart, N Dobigeon The Astronomical Journal 160 (1), 28, 2020 | 21 | 2020 |
| Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling E Monier, T Oberlin, N Brun, X Li, M Tencé, N Dobigeon Ultramicroscopy 215, 112993, 2020 | 19 | 2020 |
sylvain meignenLaboratoire LJK在 imag.fr 的电子邮件经过验证
