5 papers
Efficient Plug-and-Play method for Dynamic Imaging Via Kalman Smoothing
Benjamin Hawkes, Mike Davies, Victor Elvira +1
State-space models (SSM) are common in signal processing, where Kalman smoothing (KS) methods are state-of-the-art. However, traditional KS techniques lack expressivity as they do…
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems
Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti +1
Markov Chain Monte Carlo (MCMC) algorithms are standard approaches to solve imaging inverse problems and quantify estimation uncertainties, a key requirement in absence of ground-t…
A Unified Framework for Lifted Training and Inversion Approaches
Xiaoyu Wang, Alexandra Valavanis, Azhir Mahmood +3
The training of deep neural networks predominantly relies on a combination of gradient-based optimisation and back-propagation for the computation of the gradient. While incredibly…
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms
Matthieu Kowalski, Benoît Malézieux, Thomas Moreau +1
In this work we study the behavior of the forward-backward (FB) algorithm when the proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian denoiser, i…
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers
Xiaoyu Wang, Martin Benning, Audrey Repetti
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a…