Split Bregman algorithms for sparse group lasso with application to MRI reconstruction
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Publication:335981
DOI10.1007/s11045-014-0282-7zbMath1349.94097OpenAlexW2057943417MaRDI QIDQ335981
Publication date: 10 November 2016
Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11045-014-0282-7
Biomedical imaging and signal processing (92C55) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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- The Adaptive Lasso and Its Oracle Properties
- Bregman iteration algorithm for sparse nonnegative matrix factorizations via alternating \(l_1\)-norm minimization
- Fixed point and Bregman iterative methods for matrix rank minimization
- Geometric applications of the split Bregman method: segmentation and surface reconstruction
- Split Bregman method for large scale fused Lasso
- The benefit of group sparsity
- K-hyperline clustering learning for sparse component analysis
- A coupled variational model for image denoising using a duality strategy and split Bregman
- The Split Bregman Method for L1-Regularized Problems
- Split Bregman Methods and Frame Based Image Restoration
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- The Group Lasso for Logistic Regression
- Improved FOCUSS Method With Conjugate Gradient Iterations
- Model Selection and Estimation in Regression with Grouped Variables
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
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