CURVELET-WAVELET REGULARIZED SPLIT BREGMAN ITERATION FOR COMPRESSED SENSING
DOI10.1142/S0219691311003955zbMath1208.94017OpenAlexW2135660950MaRDI QIDQ3084700
Jianwei Ma, Gerlind Plonka-Hoch
Publication date: 25 March 2011
Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219691311003955
curveletscompressed sensingcompressive samplingalternating split Bregman iterationDouglas-Rachford split algorithmiterative shrinkage/thresholding (IST)remote sensing CS imaging
Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Numerical optimization and variational techniques (65K10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Related Items (7)
Cites Work
- Remarks on the paper by Sun and Meng, Appl. Math. Comput. 174 (2006)
- Iterative thresholding for sparse approximations
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Iterative thresholding algorithms
- Wavelets and curvelets for image deconvolution: a combined approach
- Sparse recovery by non-convex optimization - instance optimality
- Removing multiplicative noise by Douglas-Rachford splitting methods
- Wave atoms and sparsity of oscillatory patterns
- Linearized Bregman iterations for compressed sensing
- The Split Bregman Method for L1-Regularized Problems
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- Image decomposition via the combination of sparse representations and a variational approach
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Probing the Pareto Frontier for Basis Pursuit Solutions
- Split Bregman Methods and Frame Based Image Restoration
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Ten Lectures on Wavelets
- New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities
- Bayesian Compressive Sensing
- Compressed Sensing of Analog Signals in Shift-Invariant Spaces
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Solving monotone inclusions via compositions of nonexpansive averaged operators
- Model-Based Compressive Sensing
- Analysis versus synthesis in signal priors
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Signal Recovery by Proximal Forward-Backward Splitting
- Stable signal recovery from incomplete and inaccurate measurements
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Fast Discrete Curvelet Transforms
- Compressed sensing
This page was built for publication: CURVELET-WAVELET REGULARIZED SPLIT BREGMAN ITERATION FOR COMPRESSED SENSING