A smoothed \(l_0\)-norm and \(l_1\)-norm regularization algorithm for computed tomography

DOI10.1155/2019/8398035zbMath1442.94026OpenAlexW2947073678MaRDI QIDQ2337028

Jiehua Zhu, Xiezhang Li

Publication date: 19 November 2019

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2019/8398035

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Uses Software

• PDCO
• TVAL3

Cites Work

• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Existence and convergence analysis of \(\ell_{0}\) and \(\ell_{2}\) regularizations for limited-angle CT reconstruction
• Necessary and sufficient conditions of solution uniqueness in 1-norm minimization
• An efficient augmented Lagrangian method with applications to total variation minimization
• Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Decoding by Linear Programming
• Atomic Decomposition by Basis Pursuit
• A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
• On the local and global minimizers of $ \newcommand{\e}{{\rm e}} \ell_0$ gradient regularized model with box constraints for image restoration
• Sparse Approximate Solutions to Linear Systems
• Compressed sensing

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