| Parallel proximal algorithm for image restoration using hybrid regularization N Pustelnik, C Chaux, JC Pesquet IEEE transactions on Image Processing 20 (9), 2450-2462, 2011 | 189 | 2011 |
| A parallel inertial proximal optimization method JC Pesquet, N Pustelnik Preprint, 2010 | 144 | 2010 |
| Epigraphical projection and proximal tools for solving constrained convex optimization problems G Chierchia, N Pustelnik, JC Pesquet, B Pesquet-Popescu Signal, Image and Video Processing 9, 1737-1749, 2015 | 133* | 2015 |
| Nested iterative algorithms for convex constrained image recovery problems C Chaux, JC Pesquet, N Pustelnik SIAM Journal on Imaging Sciences 2 (2), 730-762, 2009 | 115 | 2009 |
| Sparse support vector machine for intrapartum fetal heart rate classification J Spilka, J Frecon, R Leonarduzzi, N Pustelnik, P Abry, M Doret IEEE journal of biomedical and health informatics 21 (3), 664-671, 2016 | 104 | 2016 |
| A nonlocal structure tensor-based approach for multicomponent image recovery problems G Chierchia, N Pustelnik, B Pesquet-Popescu, JC Pesquet IEEE transactions on Image Processing 23 (12), 5531-5544, 2014 | 90 | 2014 |
| Wavelet-based image deconvolution and reconstruction N Pustelnik, A Benazza-Benhayia, Y Zheng, JC Pesquet Wiley encyclopedia of electrical and electronics engineering, 2016 | 75 | 2016 |
| Proximal algorithms for multicomponent image recovery problems LM Briceno-Arias, PL Combettes, JC Pesquet, N Pustelnik Journal of Mathematical Imaging and Vision 41 (1), 3-22, 2011 | 75 | 2011 |
| Empirical mode decomposition revisited by multicomponent non-smooth convex optimization N Pustelnik, P Borgnat, P Flandrin Signal Processing 102, 313-331, 2014 | 64 | 2014 |
| Pursuing automated classification of historic photographic papers from raking light images CR Johnson, P Messier, WA Sethares, AG Klein, C Brown, AH Do, ... Journal of the American Institute for Conservation 53 (3), 159-170, 2014 | 48 | 2014 |
| Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization P Abry, N Pustelnik, S Roux, P Jensen, P Flandrin, R Gribonval, CG Lucas, ... Plos one 15 (8), e0237901, 2020 | 43 | 2020 |
| A primal-dual algorithm for link dependent origin destination matrix estimation G Michau, N Pustelnik, P Borgnat, P Abry, A Nantes, A Bhaskar, E Chung IEEE Transactions on Signal and Information Processing over Networks 3 (1 …, 2016 | 40 | 2016 |
| A multicomponent proximal algorithm for empirical mode decomposition N Pustelnik, P Borgnat, P Flandrin Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th …, 2012 | 39 | 2012 |
| Relaxing tight frame condition in parallel proximal methods for signal restoration N Pustelnik, JC Pesquet, C Chaux IEEE Transactions on Signal Processing 60 (2), 968-973, 2011 | 35 | 2011 |
| A deep primal-dual proximal network for image restoration M Jiu, N Pustelnik IEEE Journal of Selected Topics in Signal Processing 15 (2), 190-203, 2021 | 34 | 2021 |
| Solving inverse problems with overcomplete transforms and convex optimization techniques L Chaari, N Pustelnik, C Chaux, JC Pesquet Wavelets XIII 7446, 254-267, 2009 | 34 | 2009 |
| Proximity operator of a sum of functions; application to depth map estimation N Pustelnik, L Condat IEEE Signal Processing Letters 24 (12), 1827-1831, 2017 | 31 | 2017 |
| Instantaneous counting of components in nonstationary signals N Saulig, N Pustelnik, P Borgnat, P Flandrin, V Sucic 21st European Signal Processing Conference (EUSIPCO 2013), 1-5, 2013 | 31 | 2013 |
| Nonsmooth convex optimization for structured illumination microscopy image reconstruction J Boulanger, N Pustelnik, L Condat, L Sengmanivong, T Piolot Inverse problems 34 (9), 095004, 2018 | 29 | 2018 |
| Semi-linearized proximal alternating minimization for a discrete Mumford–Shah model M Foare, N Pustelnik, L Condat IEEE Transactions on Image Processing 29, 2176-2189, 2019 | 28 | 2019 |
patrice abryCNRS ENS LyonVerified email at ens-lyon.fr
