On the amount of regularization for super-resolution interpolation. - Yann Traonmilin; Saïd Ladjal; Andrés Almansa (EUSIPCO, 2012)

Outlier Removal Power of the L1-Norm Super-Resolution. - Yann Traonmilin; Saïd Ladjal; Andrés Almansa (SSVM, 2013)

Simultaneous High Dynamic Range and Superresolution Imaging without Regularization. - Yann Traonmilin; Cecilia Aguerrebere (SIAM J. Imaging Sci., 2014)

Relations entre le modèle d'image et le nombre de mesures pour une super-résolution fidèle. (Relations between image model and number of measures for a high fidelity super-resolution). - Yann Traonmilin (2014)

Stable recovery of low-dimensional cones in Hilbert spaces: One RIP to rule them all. - Yann Traonmilin; Rémi Gribonval (CoRR, 2015)

Robust Multi-image Processing with Optimal Sparse Regularization. - Yann Traonmilin; Saïd Ladjal; Andrés Almansa (J. Math. Imaging Vis., 2015)

A framework for low-complexity signal recovery and its application to structured sparsity. - Yann Traonmilin; Rémi Gribonval (ITW, 2016)

Phase unmixing: Multichannel source separation with magnitude constraints. - Antoine Deleforge; Yann Traonmilin (ICASSP, 2017)

Compressive K-means. - Nicolas Keriven; Nicolas Tremblay; Yann Traonmilin; Rémi Gribonval (ICASSP, 2017)

Compressive Statistical Learning with Random Feature Moments. - Rémi Gribonval; Gilles Blanchard; Nicolas Keriven; Yann Traonmilin (CoRR, 2017)

Compressed sensing in Hilbert spaces. - Yann Traonmilin; Gilles Puy; Rémi Gribonval; Mike E. Davies 001 (CoRR, 2017)

Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations. - Yann Traonmilin; Samuel Vaiter (CoRR, 2018)

Is the 1-norm the best convex sparse regularization? - Yann Traonmilin; Samuel Vaiter; Rémi Gribonval (CoRR, 2018)

The basins of attraction of the global minimizers of the non-convex sparse spikes estimation problem. - Yann Traonmilin; Jean-François Aujol (CoRR, 2018)

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. - Rémi Gribonval; Gilles Blanchard; Nicolas Keriven; Yann Traonmilin (CoRR, 2020)

The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension. - Yann Traonmilin; Jean-François Aujol; Arthur Leclaire (CoRR, 2020)

An algorithm for non-convex off-the-grid sparse spike estimation with a minimum separation constraint. - Yann Traonmilin; Jean-François Aujol; Arthur Leclaire (CoRR, 2020)

Projected Gradient Descent for Non-Convex Sparse Spike Estimation. - Yann Traonmilin; Jean-François Aujol; Arthur Leclaire (IEEE Signal Process. Lett., 2020)

Sur la performance des méthodes convexes et non-convexes de reconstruction de modèles de faible dimension en science des données. - Yann Traonmilin (2021)

Sketched Learning for Image Denoising. - Hui Shi; Yann Traonmilin; Jean-François Aujol (SSVM, 2021)

Piecewise linear prediction model for action tracking in sports. - Axel Baldanza; Jean-François Aujol; Yann Traonmilin; François Alary (EUSIPCO, 2022)

Fast off-the-grid sparse recovery with over-parametrized projected gradient descent. - Pierre-Jean Bénard; Yann Traonmilin; Jean-François Aujol (EUSIPCO, 2022)

Compressive Learning for Patch-Based Image Denoising. - Hui Shi; Yann Traonmilin; Jean-François Aujol (SIAM J. Imaging Sci., 2022)

Compressive Learning of Deep Regularization for Denoising. - Hui Shi; Yann Traonmilin; Jean-François Aujol (SSVM, 2023)

Projected Block Coordinate Descent for Sparse Spike Estimation. - Pierre-Jean Bénard; Yann Traonmilin; Jean-François Aujol (EUSIPCO, 2024)

Adaptive Parameter Selection for Gradient-Sparse Plus Low Patch-Rank Recovery: Application to Image Decomposition. - Antoine Guennec; Jean-François Aujol; Yann Traonmilin (EUSIPCO, 2024)

On Strong Basins of Attractions for Non-convex Sparse Spike Estimation: Upper and Lower Bounds. - Yann Traonmilin; Jean-François Aujol; Pierre-Jean Bénard; Arthur Leclaire (J. Math. Imaging Vis., 2024)

Batch-Less Stochastic Gradient Descent for Compressive Learning of Deep Regularization for Image Denoising. - Hui Shi; Yann Traonmilin; Jean-François Aujol (J. Math. Imaging Vis., 2024)

Parameter-Free Structure-Texture Image Decomposition by Unrolling. - Laura Girometti; Jean-François Aujol; Antoine Guennec; Yann Traonmilin (SSVM (1), 2025)

Max-Sparsity Atomic Autoencoders with Application to Inverse Problems. - Ali Joundi; Yann Traonmilin; Alasdair Newson (SSVM (1), 2025)

Joint Structure-Texture Low-Dimensional Modeling for Image Decomposition with a Plug-and-Play Framework. - Antoine Guennec; Jean-François Aujol; Yann Traonmilin (SIAM J. Imaging Sci., 2025)

Sketched Over-Parametrized Projected Gradient Descent for Sparse Spike Estimation. - Pierre-Jean Bénard; Yann Traonmilin; Jean-François Aujol (IEEE Signal Process. Lett., 2025)

On the impact of the parametrization of deep convolutional neural networks on post-training quantization. - Samy Houache; Jean-François Aujol; Yann Traonmilin (CoRR, 2025)

Stochastic Orthogonal Regularization for deep projective priors. - Ali Joundi; Yann Traonmilin; Alasdair Newson (CoRR, 2025)