Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
From MaRDI portal
Publication:5236633
DOI10.1137/18M1173629zbMath1429.94022arXiv1803.00889OpenAlexW2963031812WikidataQ128515409 ScholiaQ128515409MaRDI QIDQ5236633
Audrey Repetti, Marcelo Pereyra, Y. Wiaux
Publication date: 9 October 2019
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1803.00889
convex optimizationimage processinghypothesis testinginverse problemsBayesian inferenceuncertainty quantification
Parametric hypothesis testing (62F03) Bayesian inference (62F15) Convex programming (90C25) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Inverse problems in optimal control (49N45)
Related Items (9)
Patch-Based Image Restoration Using Expectation Propagation ⋮ Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie ⋮ The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution ⋮ On maximum a posteriori estimation with Plug \& Play priors and stochastic gradient descent ⋮ Efficient Bayesian Computation for Low-Photon Imaging Problems ⋮ Representation and reconstruction of covariance operators in linear inverse problems ⋮ Accelerating Proximal Markov Chain Monte Carlo by Using an Explicit Stabilized Method ⋮ BUQO ⋮ Solving inverse problems using data-driven models
Uses Software
Cites Work
- Unnamed Item
- 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
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators
- Variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function
- On the convergence of the iterates of the ``fast iterative shrinkage/thresholding algorithm
- Proximity for sums of composite functions
- A block coordinate variable metric forward-backward algorithm
- Dykstra's alternating projection algorithm for two sets
- An algorithm for total variation minimization and applications
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- A splitting algorithm for dual monotone inclusions involving cocoercive operators
- Convergence analysis for a primal-dual monotone + skew splitting algorithm with applications to total variation minimization
- Proximal Markov chain Monte Carlo algorithms
- A Class of Randomized Primal-Dual Algorithms for Distributed Optimization
- A Nonlocal Bayesian Image Denoising Algorithm
- iPiano: Inertial Proximal Algorithm for Nonconvex Optimization
- Proximal Splitting Methods in Signal Processing
- Large‐Scale Inverse Problems and Quantification of Uncertainty
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
- A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality
- Proximity Maps for Convex Sets
- An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
- Should Penalized Least Squares Regression be Interpreted as Maximum A Posteriori Estimation?
- Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing
- Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
- On Projection Algorithms for Solving Convex Feasibility Problems
- A Modified Forward-Backward Splitting Method for Maximal Monotone Mappings
- Solving Coupled Composite Monotone Inclusions by Successive Fejér Approximations of their Kuhn--Tucker Set
- Maximum-a-Posteriori Estimation with Bayesian Confidence Regions
- The Bayesian Choice
- Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm
- Signal Recovery by Proximal Forward-Backward Splitting
- An introduction to continuous optimization for imaging
- Convex analysis and monotone operator theory in Hilbert spaces
- Compressed sensing
This page was built for publication: Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
