On the local and global minimizers of $ \newcommand{\e}{{\rm e}} \ell_0$ gradient regularized model with box constraints for image restoration

DOI10.1088/1361-6420/aad1c5zbMath1397.49021OpenAlexW2841275796WikidataQ113091825 ScholiaQ113091825MaRDI QIDQ4582667

Chao Zeng, Xue Feng, Chunlin Wu

Publication date: 24 August 2018

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/aad1c5

zbMATH Keywords

box constraintsimage restorationlocal minimizerglobal minimizer\(\ell_0\) gradient regularization

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Nonsmooth analysis (49J52) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (6)

Two-Phase Image Segmentation by Nonconvex Nonsmooth Models with Convergent Alternating Minimization Algorithms ⋮ A General Non-Lipschitz Infimal Convolution Regularized Model: Lower Bound Theory and Algorithm ⋮ A globally convergent algorithm for a constrained non-Lipschitz image restoration model ⋮ Zero norm based analysis model for image smoothing and reconstruction ⋮ A globally convergent algorithm for a class of gradient compounded non-Lipschitz models applied to non-additive noise removal ⋮ A smoothed \(l_0\)-norm and \(l_1\)-norm regularization algorithm for computed tomography

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