Computational Aspects of Constrained L 1-L 2 Minimization for Compressive Sensing
From MaRDI portal
Publication:5356981
DOI10.1007/978-3-319-18161-5_15zbMath1370.90176OpenAlexW1165639838MaRDI QIDQ5356981
Yifei Lou, Stanley J. Osher, Jack X. Xin
Publication date: 12 September 2017
Published in: Advances in Intelligent Systems and Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-18161-5_15
Convex programming (90C25) Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items
An efficient semismooth Newton method for adaptive sparse signal recovery problems ⋮ Unconstrained \(\ell_1\)-\(\ell_2\) minimization for sparse recovery via mutual coherence ⋮ Nonconvex regularization for blurred images with Cauchy noise ⋮ Point source super-resolution via non-convex \(L_1\) based methods ⋮ Variational multiplicative noise removal by DC programming ⋮ Minimization of $L_1$ Over $L_2$ for Sparse Signal Recovery with Convergence Guarantee ⋮ A nonconvex \(l_1 (l_1-l_2)\) model for image restoration with impulse noise ⋮ A necessary and sufficient condition for sparse vector recovery via \(\ell_1-\ell_2\) minimization ⋮ Generalized sparse recovery model and its neural dynamical optimization method for compressed sensing ⋮ \(k\)-sparse vector recovery via truncated \(\ell_1 -\ell_2\) local minimization ⋮ Open issues and recent advances in DC programming and DCA ⋮ A new nonconvex approach for image restoration with Gamma noise ⋮ Smoothing techniques and difference of convex functions algorithms for image reconstructions ⋮ Fast L1-L2 minimization via a proximal operator ⋮ DC programming and DCA: thirty years of developments ⋮ DC decomposition of nonconvex polynomials with algebraic techniques ⋮ A Scale-Invariant Approach for Sparse Signal Recovery ⋮ Image restoration based on fractional-order model with decomposition: texture and cartoon ⋮ On the grouping effect of the \(l_{1-2}\) models ⋮ Efficient Boosted DC Algorithm for Nonconvex Image Restoration with Rician Noise ⋮ A new sufficient condition for sparse vector recovery via ℓ1 − ℓ2 local minimization
Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Super-resolution from noisy data
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
- Coherence Pattern–Guided Compressive Sensing with Unresolved Grids
- Sparse representations in unions of bases
- A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
- Uncertainty principles and ideal atomic decomposition
- Sparse Approximate Solutions to Linear Systems
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
- Stable signal recovery from incomplete and inaccurate measurements
- An Iterative Regularization Method for Total Variation-Based Image Restoration
- Compressed sensing