Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

• URL: http://arxiv.org/abs/2002.09611v2
• Date: Wed, 18 Nov 2020 13:33:47 GMT
• Title: Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
• Authors: Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Sch\"onlieb, Hua Huang
• Abstract summary: 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.
• Score: 22.239477171296056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Plug-and-play (PnP) is a non-convex framework that combines ADMM or other proximal algorithms with advanced denoiser priors. Recently, PnP has achieved great empirical success, especially with the integration of deep learning-based denoisers. However, a key problem of PnP based approaches is that they require manual parameter tweaking. It is necessary to obtain high-quality results across the high discrepancy in terms of imaging conditions and varying scene content. In this work, we present a tuning-free PnP proximal algorithm, which can automatically determine the internal parameters including the penalty parameter, the denoising strength and the terminal time. A key part of our approach is to develop a policy network for automatic search of parameters, which can be effectively learned via mixed model-free and model-based deep reinforcement learning. We demonstrate, through numerical and visual experiments, that the learned policy can customize different parameters for different states, and often more efficient and effective than existing handcrafted criteria. Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results. This is prevalent on both linear and nonlinear exemplary inverse imaging problems, and in particular, we show promising results on Compressed Sensing MRI 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)
• Sub-linear Regret in Adaptive Model Predictive Control [56.705978425244496]
  We present STT-MPC (Self-Tuning Tube-based Model Predictive Control), an online oracle that combines the certainty-equivalence principle and polytopic tubes. We analyze the regret of the algorithm, when compared to an algorithm initially aware of the system dynamics.
  arXiv  Detail & Related papers  (2023-10-07T15:07:10Z)
• PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval [6.982256124089]
  Phase retrieval is a challenge nonlinear inverse problem in computational imaging and image processing. We have developed PRISTA-Net, a deep unfolding network based on the first-order iterative threshold threshold algorithm (ISTA) All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold, and step size, are learned-to-end instead of being set.
  arXiv  Detail & Related papers  (2023-09-08T07:37:15Z)
• Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits [55.03293214439741]
  In contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience. We propose the first online continuous hyperparameter tuning framework for contextual bandits. We show that it could achieve a sublinear regret in theory and performs consistently better than all existing methods on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2023-02-18T23:31:20Z)
• 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)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• 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)
• Gradient Step Denoiser for convergent Plug-and-Play [5.629161809575015]
  Plug-and-Play methods can lead to tremendous visual performance for various image problems. We propose new type of Plug-and-Play methods, based on half-quadratic descent. Experiments show that it is possible to learn such a deep denoiser while not compromising the performance.
  arXiv  Detail & Related papers  (2021-10-07T07:11:48Z)
• 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)
• TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems [22.239477171296056]
  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.
  arXiv  Detail & Related papers  (2020-11-18T14:19:30Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.