Reweighted minimization model for MR image reconstruction with split Bregman method

DOI10.1007/s11432-011-4328-2zbMath1270.94028OpenAlexW2036787151MaRDI QIDQ362295

Hui Zhang, Li-Zhi Cheng, Jian-ping Li

Publication date: 20 August 2013

Published in: Science China. Information Sciences (Search for Journal in Brave)

Full work available at URL: http://engine.scichina.com/doi/10.1007/s11432-011-4328-2

zbMATH Keywords

image reconstructionsplit Bregman methodMR imagereweighed minimization

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer ⋮ Mathematical method in optical molecular imaging ⋮ Bound alternative direction optimization for image deblurring

Uses Software

• NESTA
• RecPF

Cites Work

• Geometric applications of the split Bregman method: segmentation and surface reconstruction
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Linearized Bregman iterations for compressed sensing
• NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
• The Split Bregman Method for L1-Regularized Problems
• Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Split Bregman Methods and Frame Based Image Restoration
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• Stable signal recovery from incomplete and inaccurate measurements
• Compressed sensing
• Unnamed Item

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