LLp norm regularization based group sparse representation for image compressed sensing recovery
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Summary
An efficient scheme based on split Bregman framework and half-quadratic (HQ) theory to solve the resulting optimization problem (called as RGSR- L L p ).
- Type
- article
- Published
- 2019-10-01
- Cited by
- 11
- References
- 53
- OpenAlex
- https://openalex.org/W2964979197
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:201133725
Keywords
Norm (philosophy), Mathematics, Robustness (evolution), Algorithm, Computer science
References
- Color TV: total variation methods for restoration of vector-valued images
- Adaptive sparse coding on PCA dictionary for image denoising
- Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- Nonlinear image recovery with half-quadratic regularization
- Image Compressive Sensing Recovery via Collaborative Sparsity
- Improved Image Recovery From Compressed Data Contaminated With Impulsive Noise
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing With Prior Information
- Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
- Compressive Sensing via Nonlocal Low-Rank Regularization
- Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
- Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain
- Group-Based Sparse Representation for Image Restoration
- Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization
- Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation
- Alternating Direction Algorithms for 1-Problems in Compressive Sensing
- Generalized cauchy distributions
- Constrained Restoration and the Recovery of Discontinuities
- Non-local sparse regularization model with application to image denoising
Cited by
- Nonconvex Nonsmooth Low-Rank Minimization for Generalized Image Compressed Sensing via Group Sparse Representation
- Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization
- Model-based decentralized Bayesian algorithm for distributed compressed sensing
- Scalable Image Compressed Sensing With Generator Networks
- A Lorentzian-ℓp norm regularization based algorithm for recovering sparse signals in two
- Fast fault diagnosis method of rolling bearings based on compression features in multi-sensor redundant observation environment
- Low rank and sparse decomposition based on extended LLp[12pt]minimal amsmath wasysym amsfonts amssymb amsbsy mathrsfs upgreek -69pt documentLL_p
- Natural image restoration based on multi-scale group sparsity residual constraints
- MRI reconstruction via reconciliation of low rank prior and structured sparsity
- Moving Object Detection Based on eLLp-L1-Total Variation Regularized Robust Principal Component Analysis
- Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation
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