NF-ULA: normalizing flow-based unadjusted Langevin algorithm for imaging inverse problems

Bayesian inference (62F15) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Inverse problems in optimal control (49N45)

Cites Work

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The Little Engine that Could: Regularization by Denoising (RED)
Exponential convergence of Langevin distributions and their discrete approximations
Langevin-type models. I: Diffusions with given stationary distributions and their discretizations
Sampling from non-smooth distributions through Langevin diffusion
Convolutional proximal neural networks and plug-and-play algorithms
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Proximal Markov chain Monte Carlo algorithms
Nonasymptotic bounds for sampling algorithms without log-concavity
Posterior Expectation of the Total Variation Model: Properties and Experiments
Inverse problems: A Bayesian perspective
Proximal Splitting Methods in Signal Processing
Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes
Tweedie’s Formula and Selection Bias
Markov Chains
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Deep Convolutional Neural Network for Inverse Problems in Imaging
Inverse Problem Theory and Methods for Model Parameter Estimation
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
On the Well-posedness of Bayesian Inverse Problems
On the local Lipschitz stability of Bayesian inverse problems
Stabilizing Invertible Neural Networks Using Mixture Models
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint
Generalized Normalizing Flows via Markov Chains
Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie
Modern regularization methods for inverse problems
Solving inverse problems using data-driven models
High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)
Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
Signal Recovery by Proximal Forward-Backward Splitting
Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities
Learning Maximally Monotone Operators for Image Recovery
Convex analysis and monotone operator theory in Hilbert spaces
Optimal Transport
The Langevin Monte Carlo algorithm in the non-smooth log-concave case

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