69 citations · 226 across the 28 of their papers we have counts for
30 papers
Unfolded proximal neural networks for robust image Gaussian denoising
Hoang Trieu Vy Le, Audrey Repetti, Nelly Pustelnik
A common approach to solve inverse imaging problems relies on finding a maximum a posteriori (MAP) estimate of the original unknown image, by solving a minimization problem. In thi…
IML FISTA: A Multilevel Framework for Inexact and Inertial Forward-Backward. Application to Image Restoration
Guillaume Lauga, Elisa Riccietti, Nelly Pustelnik +1
This paper presents a multilevel framework for inertial and inexact proximal algorithms, that encompasses multilevel versions of classical algorithms such as forward-backward and F…
Multilevel fista for image restoration
Guillaume Lauga, Elisa Riccietti, Nelly Pustelnik +1
This paper presents a multilevel FISTA algorithm, based on the use of the Moreau envelope to build the correction brought by the coarse models, which is easy to compute when the ex…
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
Gersende Fort, Barbara Pascal, Patrice Abry +1
Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently…
Alternative design of DeepPDNet in the context of image restoration
Mingyuan Jiu, Nelly Pustelnik
This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations whe…
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Patrice Abry, Gersende Fort, Barbara Pascal +1
Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period rem…
Hyperparameter selection for Discrete Mumford-Shah
Charles-Gérard Lucas, Barbara Pascal, Nelly Pustelnik +1
This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford-Shah functional and estimat…
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
Barbara Pascal, Patrice Abry, Nelly Pustelnik +3
Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermea…
RHAPSODIE: Reconstruction of High-contrAst Polarized SOurces and Deconvolution for cIrcumstellar Environments
Laurence Denneulin, Maud Langlois, Éric Thiébaut +1
Polarimetric imaging is one of the most effective techniques for high-contrast imaging and characterization of circumstellar environments. These environments can be characterized t…
Theoretical and numerical comparison of first-order algorithms for cocoercive equations and smooth convex optimization
Luis Briceño-Arias, Nelly Pustelnik
This paper provides a theoretical and numerical comparison of classical first-order splitting methods for solving smooth convex optimization problems and cocoercive equations. From…
Primal-dual splitting scheme with backtracking for handling with epigraphic constraint and sparse analysis regularization
Laurence Denneulin, Nelly Pustelnik, Maud Langlois +2
The convergence of many proximal algorithms involving a gradient descent relies on its Lipschitz constant. To avoid computing it, backtracking rules can be used. While such a rule…
A deep primal-dual proximal network for image restoration
Mingyuan Jiu, Nelly Pustelnik
Image restoration remains a challenging task in image processing. Numerous methods tackle this problem, often solved by minimizing a non-smooth penalized co-log-likelihood function…
Parameter-free and fast nonlinear piecewise filtering. Application to experimental physics
Barbara Pascal, Nelly Pustelnik, Patrice Abry +2
Numerous fields of nonlinear physics, very different in nature, produce signals and images, that share the common feature of being essentially constituted of piecewise homogeneous…
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation
Barbara Pascal, Samuel Vaiter, Nelly Pustelnik +1
Penalized Least Squares are widely used in signal and image processing. Yet, it suffers from a major limitation since it requires fine-tuning of the regularization parameters. Unde…
Solving NMF with smoothness and sparsity constraints using PALM
Raimon Fabregat, Nelly Pustelnik, Paulo Gonçalves +1
Non-negative matrix factorization is a problem of dimensionality reduction and source separation of data that has been widely used in many fields since it was studied in depth in 1…
Strongly Convex Optimization for Joint Fractal Feature Estimation and Texture Segmentation
Barbara Pascal, Nelly Pustelnik, Patrice Abry
The present work investigates the segmentation of textures by formulating it as a strongly convex optimization problem, aiming to favor piecewise constancy of fractal features (loc…
Combining traffic counts and Bluetooth data for link-origin-destination matrix estimation in large urban networks: The Brisbane case study
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat +3
Origin-Destination matrix estimation is a keystone for traffic representation and analysis. Traditionally estimated thanks to traffic counts, surveys and socio-economic models, rec…
Sparse hierarchical interaction learning with epigraphical projection
Mingyuan Jiu, Nelly Pustelnik, Stefan Janaqi +3
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning. We investig…
Non-Linear Wavelet Regression and Branch & Bound Optimization for the Full Identification of Bivariate Operator Fractional Brownian Motion
Jordan Frecon, Gustavo Didier, Nelly Pustelnik +1
Self-similarity is widely considered the reference framework for modeling the scaling properties of real-world data. However, most theoretical studies and their practical use have…
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon +2
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low c…
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Gabriel Michau, Nelly Pustelnik, Pierre Borgnat +4
Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic coun…
On-the-fly Approximation of Multivariate Total Variation Minimization
Jordan Frecon, Nelly Pustelnik, Patrice Abry +1
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for u…
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Nelly Pustelnik, Herwig Wendt, Patrice Abry +1
Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures an…
A Proximal Approach for Sparse Multiclass SVM
G. Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet +1
Sparsity-inducing penalties are useful tools to design multiclass support vector machines (SVMs). In this paper, we propose a convex optimization approach for efficiently and exact…