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Found 7 papers, 4 papers with code
Learning truly monotone operators with applications to nonlinear inverse problems
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss.
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.
A primal-dual data-driven method for computational optical imaging with a photonic lantern
A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy.
A practical preconditioner for wide-field continuum imaging of radio interferometric data
The celebrated CLEAN algorithm has been the cornerstone of deconvolution algorithms in radio interferometry almost since its conception in the 1970s.
Radio Interferometry
Instrumentation and Methods for Astrophysics
Learning Maximally Monotone Operators for Image Recovery
Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Automated Theorem Proving
Image Restoration
Optimization and Control
Image and Video Processing
47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times.
A Class of Randomized Primal-Dual Algorithms for Distributed Optimization
Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes, Pesquet, 2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems.
Optimization and Control 47H05, 49M29, 49M27, 65K10, 90C25
