Corpus ID: 228083511

TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems

@article{Wei2020TFPnPTP,
  title={TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems},
  author={Kaixuan Wei and Angelica I. Avil{\'e}s-Rivero and Jingwei Liang and Ying Fu and Hua Huang and Carola-Bibiane Schonlieb},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.05703}
}
Plug-and-Play (PnP) is a non-convex framework that combines proximal algorithms, for example alternating direction method of multipliers (ADMM), with advanced denoiser priors. Over the past few years, great empirical success has been obtained by PnP algorithms, especially for the ones integrated with deep learning-based denoisers. However, a crucial issue of PnP approaches is the need of manual parameter tweaking. As it is essential to obtain high-quality results across the high discrepancy in… Expand
Dynamic Proximal Unrolling Network for Compressive Imaging
TLDR
Experimental results demonstrate that the proposed DPUNet can effectively handle multiple compressive imaging modalities under varying sampling ratios and noise levels via only one trained model, and outperform the state-of-the-art approaches. Expand

References

SHOWING 1-10 OF 219 REFERENCES
On Plug-and-Play Regularization Using Linear Denoisers
TLDR
It is proved that a broader class of linear denoisers can be expressed as a proximal map of some convex regularizer, which excludes kernel denoiser such as nonlocal means that are inherently non-symmetric. Expand
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP isExpand
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective
  • S. Chan
  • Computer Science
  • IEEE Transactions on Computational Imaging
  • 2019
TLDR
This paper restricts the denoisers to the class of graph filters under a linearity assumption, or more specifically the symmetric smoothing filters, and introduces a new analysis technique via the concept of consensus equilibrium to provide interpretations to problems involving multiple priors. Expand
An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
TLDR
A new online PnP algorithm based on the proximal gradient method (PGM) is introduced, which uses only a subset of measurements at every iteration, which makes it scalable to very large datasets. Expand
Parameter-free Plug-and-Play ADMM for image restoration
  • Xiran Wang, S. Chan
  • Mathematics, Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
TLDR
This paper presents a parameter-free Plug-and-Play ADMM where internal parameters are updated as part of the optimization, derived from the generalized approximate message passing, with several essential modifications. Expand
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
TLDR
This paper studies the possibility of replacing the proximal operator of the regularization used in many convex energy minimization algorithms by a denoising neural network, and obtains state-of-the-art reconstruction results. Expand
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium
TLDR
Consensus Equilibrium (CE) is introduced, which generalizes regularized inversion to include a much wider variety of both forward components and prior components without the need for either to be expressed with a cost function. Expand
Plug-and-Play Image Restoration with Deep Denoiser Prior
TLDR
Experimental results on three representative image restoration tasks demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state- of theart learning- based methods. Expand
Primal-Dual Plug-and-Play Image Restoration
  • S. Ono
  • Mathematics, Computer Science
  • IEEE Signal Processing Letters
  • 2017
TLDR
This approach resolves issues by leveraging the nature of primal-dual splitting, yielding a very flexible plug-and-play image restoration method that is much more efficient than ADMMPnP with an inner loop and keeps the same efficiency in the case where the subproblem of ADM MPnP can be solved efficiently. Expand
MoDL: Model-Based Deep Learning Architecture for Inverse Problems
TLDR
This work introduces a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior, and proposes to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. Expand
...
1
2
3
4
5
...