Reducing effects of bad data using variance based joint sparsity recovery
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Publication:1736886
DOI10.1007/s10915-018-0754-2zbMath1410.65120OpenAlexW2808272556MaRDI QIDQ1736886
Publication date: 26 March 2019
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-018-0754-2
Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10)
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Cites Work
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- Image reconstruction from undersampled Fourier data using the polynomial annihilation transform
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Algorithms for simultaneous sparse approximation. I: Greedy pursuit
- Algorithms for simultaneous sparse approximation. II: Convex relaxation
- Composite SAR imaging using sequential joint sparsity
- Simultaneous approximation by greedy algorithms
- Introduction to Nonlinear Optimization
- A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
- Iteratively reweighted least squares minimization for sparse recovery
- Two-Point Step Size Gradient Methods
- Numerical Optimization
- High-Order Total Variation-Based Image Restoration
- An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
- Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
- Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
- Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
- Joint Sparse Recovery Based on Variances
- Robust Recovery of Signals From a Structured Union of Subspaces
- Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation
- Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices
- Sparse solutions to linear inverse problems with multiple measurement vectors
- Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images
- Edge Detection of Piecewise Smooth Functions from UnderSampled Fourier Data Using Variance Signatures
- Convex analysis and monotone operator theory in Hilbert spaces
- Convex functions and their applications. A contemporary approach