Image denoising using combined higher order non-convex total variation with overlapping group sparsity
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Publication:1710972
DOI10.1007/s11045-018-0567-3zbMath1429.94009OpenAlexW2792927489WikidataQ130115935 ScholiaQ130115935MaRDI QIDQ1710972
Raveendran Paramesran, Tarmizi Adam
Publication date: 24 January 2019
Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11045-018-0567-3
Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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Cites Work
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Nonlinear total variation based noise removal algorithms
- Image restoration using total variation with overlapping group sparsity
- Fast minimization methods for solving constrained total-variation superresolution image reconstruction
- An alternating direction algorithm for matrix completion with nonnegative factors
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Convergence of the reweighted \(\ell_1\) minimization algorithm for \(\ell_2-\ell_p\) minimization
- Global convergence of ADMM in nonconvex nonsmooth optimization
- A parallel alternating direction method with application to compound \(l_{1}\)-regularized imaging inverse problems
- A combined first and second order variational approach for image reconstruction
- A coupled variational model for image denoising using a duality strategy and split Bregman
- Fourth-order partial differential equations for noise removal
- Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers
- An alternating direction method for total variation denoising
- Alternating direction methods for classical and ptychographic phase retrieval
- Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time
- Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
- Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
- High-Order Total Variation-Based Image Restoration
- Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
- Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
- Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
- Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems
- Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction
- An introduction to continuous optimization for imaging
- Total Generalized Variation
