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07/30/2023
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Combining the strengths of model-based iterative algorithms and data-dri...
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04/17/2023
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative...
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03/09/2023
Provably Convergent Plug-and-Play Quasi-Newton Methods
Plug-and-Play (PnP) methods are a class of efficient iterative methods t...
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08/31/2022
Accelerating Deep Unrolling Networks via Dimensionality Reduction
In this work we propose a new paradigm for designing efficient deep unro...
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08/02/2022
Stochastic Primal-Dual Three Operator Splitting with Arbitrary Sampling and Preconditioning
In this work we propose a stochastic primal-dual preconditioned three-op...
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03/21/2022
Operator Sketching for Deep Unrolling Networks
In this work we propose a new paradigm for designing efficient deep unro...
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03/14/2022
Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients
In this work we propose a new paradigm for designing fast plug-and-play ...
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02/22/2022
Data-Consistent Local Superresolution for Medical Imaging
In this work we propose a new paradigm of iterative model-based reconstr...
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02/10/2022
Equivariance Regularization for Image Reconstruction
In this work, we propose Regularization-by-Equivariance (REV), a novel s...
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10/19/2021
Stochastic Primal-Dual Deep Unrolling Networks for Imaging Inverse Problems
In this work we present a new type of efficient deep-unrolling networks ...
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06/20/2020
A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems
In this work we propose an efficient stochastic plug-and-play (PnP) algo...
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06/03/2020
CNN Denoisers As Non-Local Filters: The Neural Tangent Denoiser
Convolutional Neural Networks (CNNs) are now a well-established tool for...
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10/22/2019
