| Results of the 2020 fastMRI challenge for machine learning MR image reconstruction MJ Muckley, B Riemenschneider, A Radmanesh, S Kim, G Jeong, J Ko, ... IEEE transactions on medical imaging 40 (9), 2306-2317, 2021 | 270 | 2021 |
| A robust method to count and locate audio sources in a multichannel underdetermined mixture S Arberet, R Gribonval, F Bimbot IEEE Transactions on Signal Processing 58 (1), 121-133, 2009 | 150 | 2009 |
| Nonnegative matrix factorization and spatial covariance model for under-determined reverberant audio source separation S Arberet, A Ozerov, NQK Duong, E Vincent, R Gribonval, F Bimbot, ... 10th International Conference on Information Science, Signal Processing and …, 2010 | 111 | 2010 |
| Compressive source separation: Theory and methods for hyperspectral imaging M Golbabaee, S Arberet, P Vandergheynst IEEE Transactions on Image Processing 22 (12), 5096-5110, 2013 | 97 | 2013 |
| Diagnostic confidence and feasibility of a deep learning accelerated HASTE sequence of the abdomen in a single breath-hold J Herrmann, S Gassenmaier, D Nickel, S Arberet, S Afat, A Lingg, ... Investigative radiology 56 (5), 313-319, 2021 | 77 | 2021 |
| Underdetermined instantaneous audio source separation via local Gaussian modeling E Vincent, S Arberet, R Gribonval Independent Component Analysis and Signal Separation: 8th International …, 2009 | 71 | 2009 |
| A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture S Arberet, R Gribonval, F Bimbot International Conference on Independent Component Analysis and Signal …, 2006 | 51 | 2006 |
| State-of-the-art machine learning MRI reconstruction in 2020: Results of the second fastMRI challenge MJ Muckley, B Riemenschneider, A Radmanesh, S Kim, G Jeong, J Ko, ... arXiv preprint arXiv:2012.06318 2 (6), 7, 2020 | 44 | 2020 |
| Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning–based image reconstruction: qualitative and quantitative comparison of image quality … K Shanbhogue, A Tong, P Smereka, D Nickel, S Arberet, R Anthopolos, ... European radiology 31 (11), 8447-8457, 2021 | 42 | 2021 |
| Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet S Arberet, M Lemay, P Renevey, J Sola, O Grossenbacher, D Andries, ... Computing in Cardiology 2013, 935-938, 2013 | 38 | 2013 |
| Analysis of a deep learning-based superresolution algorithm tailored to partial fourier gradient echo sequences of the abdomen at 1.5 T: reduction of breath-hold time and … S Afat, D Wessling, C Afat, D Nickel, S Arberet, J Herrmann, AE Othman, ... Investigative Radiology 57 (3), 157-162, 2022 | 33 | 2022 |
| Development and evaluation of deep learning-accelerated single-breath-hold abdominal HASTE at 3 T using variable refocusing flip angles J Herrmann, D Nickel, JP Mugler III, S Arberet, S Gassenmaier, S Afat, ... Investigative Radiology 56 (10), 645-652, 2021 | 33 | 2021 |
| A robust method to count and locate audio sources in a stereophonic linear anechoic mixture S Arberet, R Gribonval, F Bimbot 2007 IEEE International Conference on Acoustics, Speech and Signal …, 2007 | 33 | 2007 |
| Sparse reverberant audio source separation via reweighted analysis S Arberet, P Vandergheynst, RE Carrillo, JP Thiran, Y Wiaux IEEE Transactions on Audio, Speech, and Language Processing 21 (7), 1391-1402, 2013 | 30 | 2013 |
| Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: comparison with conventional diffusion weighted imaging SH Bae, J Hwang, SS Hong, EJ Lee, J Jeong, T Benkert, JK Sung, ... European journal of radiology 154, 110428, 2022 | 28 | 2022 |
| Distributed compressed sensing of hyperspectral images via blind source separation M Golbabaee, S Arberet, P Vandergheynst 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals …, 2010 | 24 | 2010 |
| Blind spectral-GMM estimation for underdetermined instantaneous audio source separation S Arberet, A Ozerov, R Gribonval, F Bimbot Independent Component Analysis and Signal Separation: 8th International …, 2009 | 24 | 2009 |
| Multichannel compressed sensing via source separation for hyperspectral images M Golbabaee, S Arberet, P Vandergheynst 2010 18th European Signal Processing Conference, 1326-1329, 2010 | 19 | 2010 |
| Combined deep learning-based super-resolution and partial fourier reconstruction for gradient echo sequences in abdominal MRI at 3 Tesla: shortening breath-hold time and … H Almansour, J Herrmann, S Gassenmaier, A Lingg, MD Nickel, ... Academic radiology 30 (5), 863-872, 2023 | 17 | 2023 |
| A tractable framework for estimating and combining spectral source models for audio source separation S Arberet, A Ozerov, F Bimbot, R Gribonval Signal Processing 92 (8), 1886-1901, 2012 | 14 | 2012 |
Rémi GribonvalInria & ENS de LyonVerificeret mail på ens-lyon.fr
