A globally convergent algorithm for a constrained non-Lipschitz image restoration model
From MaRDI portal
Publication:2173566
DOI10.1007/s10915-020-01190-4zbMath1455.94037OpenAlexW3013818496WikidataQ113106891 ScholiaQ113106891MaRDI QIDQ2173566
Xue-Cheng Tai, Weina Wang, Chunlin Wu
Publication date: 16 April 2020
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-020-01190-4
image restorationlower bound theoryKurdyka-Łojasiewicz propertynon-Lipschitz optimizationsupport shrinkingbox-constrainedhigh order regularization
Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (7)
Two-Phase Image Segmentation by Nonconvex Nonsmooth Models with Convergent Alternating Minimization Algorithms ⋮ A General Non-Lipschitz Infimal Convolution Regularized Model: Lower Bound Theory and Algorithm ⋮ A convergent iterative support shrinking algorithm for non-Lipschitz multi-phase image labeling model ⋮ A nonconvex truncated regularization and box-constrained model for CT reconstruction ⋮ An accelerated smoothing gradient method for nonconvex nonsmooth minimization in image processing ⋮ A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization ⋮ Extrapolated smoothing descent algorithm for constrained nonconvex and nonsmooth composite problems
Uses Software
Cites Work
- Nonlinear total variation based noise removal algorithms
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Anisotropic total variation filtering
- Convergence of the reweighted \(\ell_1\) minimization algorithm for \(\ell_2-\ell_p\) minimization
- Variational methods on the space of functions of bounded Hessian for convexification and denoising
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- Image recovery via total variation minimization and related problems
- An iterative support shrinking algorithm for non-Lipschitz optimization in image restoration
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- On the discontinuity of images recovered by noncovex nonsmooth regularized isotropic models with box constraints
- Iterative image restoration combining total variation minimization and a second-order functional
- Constrained TV\(_p\)-\(\ell_2\) model for image restoration
- Fourth-order partial differential equations for noise removal
- Optimality Conditions and a Smoothing Trust Region Newton Method for NonLipschitz Optimization
- Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers
- Nonconvex TV$^q$-Models in Image Restoration: Analysis and a Trust-Region Regularization--Based Superlinearly Convergent Solver
- Proximity algorithms for image models: denoising
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ Minimization
- Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
- Linearly Constrained Non-Lipschitz Optimization for Image Restoration
- Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Edge-preserving and scale-dependent properties of total variation regularization
- High-Order Total Variation-Based Image Restoration
- On the local and global minimizers of $ \newcommand{\e}{{\rm e}} \ell_0$ gradient regularized model with box constraints for image restoration
- On the Edge Recovery Property of Noncovex Nonsmooth Regularization in Image Restoration
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
- Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction
- Non-Lipschitz $\ell_{p}$-Regularization and Box Constrained Model for Image Restoration
- Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
- Analysis of the Recovery of Edges in Images and Signals by Minimizing Nonconvex Regularized Least-Squares
- Sparse Signal Reconstruction via Iterative Support Detection
This page was built for publication: A globally convergent algorithm for a constrained non-Lipschitz image restoration model
