The Little Engine that Could: Regularization by Denoising (RED)
From MaRDI portal
Publication:148569
DOI10.48550/arXiv.1611.02862zbMath1401.62101arXiv1611.02862MaRDI QIDQ148569
Michael Elad, Peyman Milanfar, Yaniv Romano, Yaniv Romano, Peyman Milanfar, Michael Elad
Publication date: 9 November 2016
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1611.02862
Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items
Optimal Dirichlet control of partial differential equations on networks, Speckle reduction in matrix-log domain for synthetic aperture radar imaging, Regularization methods in the analysis of a series of scintillation fluorescence microscopy images, Regularization theory of the analytic deep prior approach, Deep learning for inverse problems. Abstracts from the workshop held March 7--13, 2021 (hybrid meeting), Patch-Based Image Restoration Using Expectation Propagation, Designing rotationally invariant neural networks from PDEs and variational methods, Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior, Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks, An Unrolled Implicit Regularization Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging with Convergence Guarantee, An introduction to the mathematics of deep learning, A literature survey of matrix methods for data science, Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper, Deep-plug-and-play proximal Gauss-Newton method with applications to nonlinear, ill-posed inverse problems, Multi-frame super resolution via deep plug-and-play CNN regularization, Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling, Self-Supervised Deep Learning for Image Reconstruction: A Langevin Monte Carlo Approach, Regularising inverse problems with generative machine learning models, On maximum a posteriori estimation with Plug \& Play priors and stochastic gradient descent, WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution, Inversion of Band-Limited Discrete Fourier Transforms of Binary Images: Uniqueness and Algorithms, Explainable bilevel optimization: an application to the Helsinki Deblur Challenge, Improving image deblurring, RTR-DPD: reweighted Tikohonov regularization for blind deblurring via dual principles of discriminativeness, PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent, Computational Imaging Through Atmospheric Turbulence, NETT: solving inverse problems with deep neural networks, The mathematics of artificial intelligence, Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method, Compressive Computed Tomography Reconstruction through Denoising Approximate Message Passing, Deep synthesis network for regularizing inverse problems, Convolutional proximal neural networks and plug-and-play algorithms, Unnamed Item, Blind Poissonian image deblurring regularized by a denoiser constraint and deep image prior, A support-denoiser-driven framework for single image restoration, Whiteness constraints in a unified variational framework for image restoration, redR, Effective new methods for automated parameter selection in regularized inverse problems, An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography, Plug-and-Play Unplugged: Optimization-Free Reconstruction Using Consensus Equilibrium, Denoising AMP for MRI Reconstruction: BM3D-AMP-MRI, Modern regularization methods for inverse problems, Solving inverse problems using data-driven models, Rendition: Reclaiming What a Black Box Takes Away, Remove the salt and pepper noise based on the high order total variation and the nuclear norm regularization, Regularization by Denoising via Fixed-Point Projection (RED-PRO), Joint Gaussian dictionary learning and tomographic reconstruction, Constrained and unconstrained deep image prior optimization models with automatic regularization
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nonlinear total variation based noise removal algorithms
- Local and nonlocal discrete regularization on weighted graphs for image and mesh processing
- Nonlocal discrete \(p\)-Laplacian driven image and manifold processing
- Image restoration via simultaneous sparse coding: where structured sparsity meets Gaussian scale mixture
- Perturbation of the eigenvectors of the graph Laplacian: application to image denoising
- Non-local regularization of inverse problems
- Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Symmetrizing Smoothing Filters
- Secrets of image denoising cuisine
- Boosting of Image Denoising Algorithms
- Sparse and Redundant Representations
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Global Image Denoising
- Progressive Image Denoising Through Hybrid Graph Laplacian Regularization: A Unified Framework
- Progressive Image Denoising
- A General Framework for Regularized, Similarity-Based Image Restoration
- Symmetric Smoothing Filters From Global Consistency Constraints
- Postprocessing of Compressed Images via Sequential Denoising
- Higher-Order Feature-Preserving Geometric Regularization
- Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
- Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising
- Is Denoising Dead?
- Fast Image Recovery Using Variable Splitting and Constrained Optimization
- An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
- Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
- Patch-Based Near-Optimal Image Denoising
- BM3D Frames and Variational Image Deblurring
- Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
- Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling
- How to SAIF-ly Boost Denoising Performance
- Nonlocally Centralized Sparse Representation for Image Restoration
- A Review of Image Denoising Algorithms, with a New One
