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Laurent Condat
Laurent Condat
Senior Research Scientist, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
在 kaust.edu.sa 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
A primal–dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
L Condat
Journal of optimization theory and applications 158 (2), 460-479, 2013
9882013
Fast projection onto the simplex and the l1 ball
L Condat
Mathematical Programming 158 (1), 575-585, 2016
4822016
A direct algorithm for 1-D total variation denoising
L Condat
IEEE Signal Processing Letters 20 (11), 1054-1057, 2013
4032013
Indusion: Fusion of multispectral and panchromatic images using the induction scaling technique
MM Khan, J Chanussot, L Condat, A Montanvert
IEEE Geoscience and Remote Sensing Letters 5 (1), 98-102, 2008
2372008
Discrete total variation: New definition and minimization
L Condat
SIAM Journal on Imaging Sciences 10 (3), 1258-1290, 2017
1622017
A new pansharpening method based on spatial and spectral sparsity priors
X He, L Condat, JM Bioucas-Dias, J Chanussot, J Xia
IEEE Transactions on Image Processing 23 (9), 4160-4174, 2014
1582014
A generic proximal algorithm for convex optimization—application to total variation minimization
L Condat
IEEE Signal Processing Letters 21 (8), 985-989, 2014
1352014
A forward-backward view of some primal-dual optimization methods in image recovery
PL Combettes, L Condat, JC Pesquet, BC Vũ
2014 IEEE International Conference on Image Processing (ICIP), 4141-4145, 2014
1202014
From Local SGD to local fixed-point methods for federated learning
G Malinovskiy, D Kovalev, E Gasanov, L Condat, P Richtárik
International Conference on Machine Learning (ICML), PMLR 119, 6692-6701, 2020
1162020
Proximal splitting algorithms for convex optimization: A tour of recent advances, with new twists
L Condat, D Kitahara, A Contreras, A Hirabayashi
SIAM Review 65 (2), 375-435, 2023
113*2023
Cadzow denoising upgraded: A new projection method for the recovery of Dirac pulses from noisy linear measurements
L Condat, A Hirabayashi
Sampling Theory in Signal and Image Processing 14 (1), 17-47, 2015
992015
Joint demosaicking and denoising by total variation minimization
L Condat, S Mosaddegh
2012 19th IEEE International Conference on Image Processing (ICIP), 2781-2784, 2012
882012
Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction
GA Licciardi, MM Khan, J Chanussot, A Montanvert, L Condat, C Jutten
EURASIP Journal on Advances in Signal processing 2012, 1-17, 2012
792012
A generic variational approach for demosaicking from an arbitrary color filter array
L Condat
2009 16th IEEE International Conference on Image Processing (ICIP), 1625-1628, 2009
692009
A new color filter array with optimal properties for noiseless and noisy color image acquisition
L Condat
IEEE Transactions on image processing 20 (8), 2200-2210, 2011
552011
Beyond interpolation: Optimal reconstruction by quasi-interpolation
L Condat, T Blu, M Unser
2005 IEEE International Conference on Image Processing (ICIP) 1, I-33, 2005
532005
Optimal gradient compression for distributed and federated learning
A Albasyoni, M Safaryan, L Condat, P Richtárik
arXiv preprint arXiv:2010.03246, 2020
522020
Quasi-interpolating spline models for hexagonally-sampled data
L Condat, D Van De Ville
IEEE Transactions on Image Processing 16 (5), 1195-1206, 2007
522007
A simple, fast and efficient approach to denoisaicking: Joint demosaicking and denoising
L Condat
2010 IEEE International Conference on Image Processing (ICIP), 905-908, 2010
462010
Dualize, split, randomize: Toward fast nonsmooth optimization algorithms
A Salim, L Condat, K Mishchenko, P Richtárik
Journal of Optimization Theory and Applications 195 (1), 102-130, 2022
392022
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