A new hybrid regularization scheme for removing salt and pepper noise

DOI10.1007/s40314-022-01869-4zbMath1499.94009OpenAlexW4280654208MaRDI QIDQ2140817

Lin He, Haohui Zhu, Bao-Li Shi, Jia-li Zhang

Publication date: 23 May 2022

Published in: Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40314-022-01869-4

zbMATH Keywords

image restorationalternating direction method of multipliers (ADMM)high order total variation overlapping group sparsity (HOTVOGS)nuclear norm (NN) regularizationsalt and pepper noise (SPN)

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Image restoration using total variation with overlapping group sparsity
• A dual algorithm for the solution of nonlinear variational problems via finite element approximation
• Image recovery via total variation minimization and related problems
• Regularization algorithms for ill-posed problems
• A survey on some recent developments of alternating direction method of multipliers
• A new impulse detection and filtering method for removal of wide range impulse noises
• Douglas-Rachford splitting and ADMM for pathological convex optimization
• High-order TVL1-based images restoration and spatially adapted regularization parameter selection
• Splitting Methods in Communication, Imaging, Science, and Engineering
• A New Detail-Preserving Regularization Scheme
• Introduction to Nonlinear Optimization
• A Singular Value Thresholding Algorithm for Matrix Completion
• A Fast $\ell$1-TV Algorithm for Image Restoration
• Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
• Convex Optimization in Signal Processing and Communications
• Higher-Order Total Directional Variation: Analysis
• Higher-order total variation approaches and generalisations
• Solving inverse problems using data-driven models
• Aspects of Total Variation RegularizedL¹Function Approximation
• The Total Variation Regularized $L^1$ Model for Multiscale Decomposition
• Total Generalized Variation
• Directional Total Generalized Variation Regularization for Impulse Noise Removal
• Matrix Completion Methods for Causal Panel Data Models
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

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