Laurent Condat
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Citado por
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
A Direct Algorithm for 1-D Total Variation Denoising
L Condat
IEEE Signal Process. Lett. 20 (11), 1054-1057, 2013
Fast projection onto the simplex and the l1 ball
L Condat
Mathematical Programming 158 (1-2), 575-585, 2016
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
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors
X He, L Condat, JM Bioucas-Dias, J Chanussot, J Xia
IEEE Trans. Image Processing 23 (9), 4160-4174, 2014
A generic proximal algorithm for convex optimization—application to total variation minimization
L Condat
IEEE Signal Processing Letters 21 (8), 985-989, 2014
A forward-backward view of some primal-dual optimization methods in image recovery
PL Combettes, L Condat, JC Pesquet, BC Vũ
IEEE International Conference on Image Processing (ICIP), 4141-4145, 2014
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), 1-17, 2012
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
A generic variational approach for demosaicking from an arbitrary color filter array
L Condat
IEEE International Conference on Image Processing (ICIP), 1625-1628, 2009
Quasi-interpolating spline models for hexagonally-sampled data
L Condat, D Van De Ville
IEEE Transactions on Image Processing 16 (5), 1195-1206, 2007
Beyond interpolation: Optimal reconstruction by quasi-interpolation
L Condat, T Blu, M Unser
IEEE International Conference on Image Processing (ICIP) 1, I-33, 2005
Discrete total variation: New definition and minimization
L Condat
SIAM Journal on Imaging Sciences 10 (3), 1258-1290, 2017
Joint Demosaicking and Denoising by Total Variation Minimization
L Condat, S Mosaddegh
IEEE International Conference on Image Processing (ICIP), 2012
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
A simple, fast and efficient approach to denoisaicking: Joint demosaicking and denoising
L Condat
IEEE International Conference on Image Processing (ICIP), 905-908, 2010
Hexagonal versus orthogonal lattices: A new comparison using approximation theory
L Condat, D Van De Ville, T Blu
IEEE International Conference on Image Processing (ICIP) 3, III-1116, 2005
Three-directional box-splines: Characterization and efficient evaluation
L Condat, D Van De Ville
IEEE Signal Processing Letters 13 (7), 417-420, 2006
A Simple Trick to Speed Up and Improve the Non-Local Means
L Condat
Color filter array design using random patterns with blue noise chromatic spectra
L Condat
Image and Vision Computing 28 (8), 1196-1202, 2010
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