Explicit iteration schemes for minimization problems arising from image denoising

DOI10.1016/j.acha.2011.04.006zbMath1229.65098OpenAlexW2037910772MaRDI QIDQ643648

Rong-Qing Jia

Publication date: 2 November 2011

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2011.04.006

zbMATH Keywords

total variationconvergenceconvex optimizationiteration schemematrix splittingGauss-Seidel methodimage denoisingsplit Bregman method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

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• Nonlinear total variation based noise removal algorithms
• Convergence analysis of the Bregman method for the variational model of image denoising
• A fast algorithm for the total variation model of image denoising
• Proximity algorithms for image models: denoising
• Relaxation Methods for Image Denoising Based on Difference Schemes
• The Split Bregman Method for L1-Regularized Problems
• Split Bregman Methods and Frame Based Image Restoration
• Global and Superlinear Convergence of Inexact Uzawa Methods for Saddle Point Problems with Nondifferentiable Mappings

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