An \(\ell^p\)-\(\ell^q\) minimization method with cross-validation for the restoration of impulse noise contaminated images

DOI10.1016/j.cam.2020.112824OpenAlexW3008873310WikidataQ113103580 ScholiaQ113103580MaRDI QIDQ2309287

Alessandro Buccini, Lothar Reichel

Publication date: 30 March 2020

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

Full work available at URL: https://doi.org/10.1016/j.cam.2020.112824

zbMATH Keywords

iterative methodminimizationill-posed problem

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Iterative numerical methods for linear systems (65F10) Numerical computation of matrix norms, conditioning, scaling (65F35) Numerical methods for inverse problems for integral equations (65R32)

Related Items (12)

Generalized cross validation for \(\ell^p\)-\(\ell^q\) minimization ⋮ Graph Laplacian for image deblurring ⋮ Variable selection in saturated and supersaturated designs via lp-lq minimization ⋮ Some numerical aspects of Arnoldi-Tikhonov regularization ⋮ Large-scale regression with non-convex loss and penalty ⋮ Impulse noise removal by using a nonconvex TGV regularizer and nonconvex fidelity ⋮ Modulus-based iterative methods for constrained ℓ _p – ℓ _q minimization ⋮ On the choice of regularization matrix for an \(\ell_2\)-\(\ell_q\) minimization method for image restoration ⋮ Fast alternating direction multipliers method by generalized Krylov subspaces ⋮ A comparison of parameter choice rules for \(\ell^p\)-\(\ell^q\) minimization ⋮ Krylov subspace split Bregman methods ⋮ A variational non-linear constrained model for the inversion of FDEM data*

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