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Found 16 papers, 4 papers with code
Equivariant plug-and-play image reconstruction
Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser.
PNN: From proximal algorithms to robust unfolded image denoising networks and Plug-and-Play methods
In particular, proximal neural networks (PNNs) have been introduced, obtained by unrolling a proximal algorithm as for finding a MAP estimate, but over a fixed number of iterations, with learned linear operators and parameters.
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators.
Alternative design of DeepPDNet in the context of image restoration
Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database.
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation.
Hyperparameter selection for Discrete Mumford-Shah
This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection.
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
The present work aims to overcome these limitations by carefully crafting a functional permitting to estimate jointly, in a single step, the reproduction number and outliers defined to model low quality data.
A deep primal-dual proximal network for image restoration
In this work, we design a deep network, named DeepPDNet, built from primal-dual proximal iterations associated with the minimization of a standard penalized likelihood with an analysis prior, allowing us to take advantage of both worlds.
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation
This work extends the Stein's Unbiased GrAdient estimator of the Risk of Deledalle et al. to the case of correlated Gaussian noise, deriving a general automatic tuning of regularization parameters.
Solving NMF with smoothness and sparsity constraints using PALM
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 1999 by Lee and Seung, including in compression of data, document clustering, processing of audio spectrograms and astronomy.
Sparse hierarchical interaction learning with epigraphical projection
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning.
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.
On-the-fly Approximation of Multivariate Total Variation Minimization
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 univariate data.
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to many applications.
A Proximal Approach for Sparse Multiclass SVM
In this paper, we propose a convex optimization approach for efficiently and exactly solving the multiclass SVM learning problem involving a sparse regularization and the multiclass hinge loss formulated by Crammer and Singer.
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image.
