Plug-and-Play ADMM for MRI Reconstruction With Convex Nonconvex Sparse Regularization.
Jincheng LiJinlan LiZhaoyang XieJian ZouPublished in: IEEE Access (2021)
Keyphrases
- alternating direction method of multipliers
- image restoration and reconstruction
- basis pursuit
- convex optimization
- compressed sensing
- augmented lagrangian method
- image reconstruction
- convex optimization problems
- structured sparsity
- total variation
- compressive sensing
- magnetic resonance imaging
- low rank
- group lasso
- denoising
- convex relaxation
- high resolution
- norm minimization
- nuclear norm
- sparse approximation
- magnetic resonance images
- rank minimization
- mixed norm
- natural images
- medical images
- sparsity inducing
- low rank matrix
- phase unwrapping
- minimization problems
- sparsity regularization
- augmented lagrangian
- signal reconstruction
- low rank matrices
- sparsity constraints
- random projections
- positron emission tomography
- primal dual
- mr images
- sparse representation
- horizontal and vertical projections
- risk minimization
- interior point methods
- image denoising
- high dimensional
- quadratic optimization problems
- image restoration
- globally convergent
- regularization term
- image processing
- robust principal component analysis
- medical imaging
- convex functions
- matrix completion
- discrete tomography
- trace norm
- convex sets
- sparse coding
- convex hull
- total variation regularization
- elastic net
- objective function
- image segmentation