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

• URL: http://arxiv.org/abs/2012.05703v3
• Date: Sat, 18 Sep 2021 04:20:19 GMT
• Title: TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
• Authors: Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Sch\"onlieb
• Abstract summary: Plug-and-Play (MM) is a non- optimization framework that combines numerical algorithms, for example, with advanced denoising priors. We discuss several practical considerations of more denoisers, which together with our learned strategies are state-of-the-art results.
• Score: 22.239477171296056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal algorithms, for example, the alternating direction method of multipliers (ADMM), with advanced denoising priors. Over the past few years, great empirical success has been obtained by PnP algorithms, especially for the ones that integrate deep learning-based denoisers. However, a key challenge of PnP approaches is the need for manual parameter tweaking as it is essential to obtain high-quality results across the high discrepancy in imaging conditions and varying scene content. In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically. A core part of our approach is a policy network for automated parameter search which can be effectively learned via a mixture of model-free and model-based deep reinforcement learning strategies. We demonstrate, through rigorous numerical and visual experiments, that the learned policy can customize parameters to different settings, and is often more efficient and effective than existing handcrafted criteria. Moreover, we discuss several practical considerations of PnP denoisers, which together with our learned policy yield state-of-the-art results. This advanced performance is prevalent on both linear and nonlinear exemplar inverse imaging problems, and in particular shows promising results on compressed sensing MRI, sparse-view CT, single-photon imaging, and phase retrieval.

Related papers

• PnP-Flow: Plug-and-Play Image Restoration with Flow Matching [2.749898166276854]
  We introduce Plug-and-Play Flow Matching, an algorithm for solving inverse imaging problems. We evaluate its performance on denoising, superresolution, and inpainting tasks.
  arXiv  Detail & Related papers  (2024-10-03T12:13:56Z)
• Modality-Agnostic Variational Compression of Implicit Neural Representations [96.35492043867104]
  We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR) Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression.
  arXiv  Detail & Related papers  (2023-01-23T15:22:42Z)
• Meta-Learning Digitized-Counterdiabatic Quantum Optimization [3.0638256603183054]
  We tackle the problem of finding suitable initial parameters for variational optimization by employing a meta-learning technique using recurrent neural networks. We investigate this technique with the recently proposed digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA) The combination of meta learning and DC-QAOA enables us to find optimal initial parameters for different models, such as MaxCut problem and the Sherrington-Kirkpatrick model.
  arXiv  Detail & Related papers  (2022-06-20T18:57:50Z)
• Online Deep Equilibrium Learning for Regularization by Denoising [20.331171081002957]
  Plug-and-Play Equilibrium Priors (memory) and Regularization by Denoising (RED) are widely-used frameworks for solving inverse imaging problems by computing fixed-points. We propose ODER as a new strategy for improving the efficiency of DEQ/RED on the total number of measurements. Our numerical results suggest the potential improvements in training/testing complexity due to ODER on three distinct imaging applications.
  arXiv  Detail & Related papers  (2022-05-25T21:06:22Z)
• Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration [54.23646128082018]
  We extend the concept of plug-and-play optimization to use denoisers that can be parameterized for non-constant noise variance. We show that our pixel-wise adjustable denoiser, along with a suitable preconditioning strategy, can further improve the plug-and-play ADMM approach for several applications.
  arXiv  Detail & Related papers  (2021-10-01T15:46:35Z)
• Learning Sampling Policy for Faster Derivative Free Optimization [100.27518340593284]
  We propose a new reinforcement learning based ZO algorithm (ZO-RL) with learning the sampling policy for generating the perturbations in ZO optimization instead of using random sampling. Our results show that our ZO-RL algorithm can effectively reduce the variances of ZO gradient by learning a sampling policy, and converge faster than existing ZO algorithms in different scenarios.
  arXiv  Detail & Related papers  (2021-04-09T14:50:59Z)
• Logistic Q-Learning [87.00813469969167]
  We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The main feature of our algorithm is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error.
  arXiv  Detail & Related papers  (2020-10-21T17:14:31Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)
• A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems [5.025654873456756]
  We propose an efficient plug-and-play ( inverse problems) algorithm for imaging applications. Our results demonstrate effectiveness of our approach compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-20T18:03:52Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems [22.239477171296056]
  Plug-and-play () is a non customize framework that combines different learned algorithms with advanced denoiser priors. A key problem. based approaches is that they require manual parameter tweaking. A key part of our approach is develop a network policy for automatic tuning of parameters. We show promising results on Compressed Sensing and phase retrieval.
  arXiv  Detail & Related papers  (2020-02-22T03:09:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.