A new method for parameter estimation of edge-preserving regularization in image restoration

DOI10.1016/j.cam.2008.08.013zbMath1156.94006OpenAlexW2143675749WikidataQ113103593 ScholiaQ113103593MaRDI QIDQ1006010

Li Gao, Xiaojuan Gu

Publication date: 17 March 2009

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.2008.08.013

zbMATH Keywords

parameter estimationGaussian noiseimage restorationedge-preserving regularizationconstrained optimization problem

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (6)

Chebyshev-Legendre spectral method and inverse problem analysis for the space fractional Benjamin-Bona-Mahony equation ⋮ Deconvolution of defocused image with multivariate local polynomial regression and iterative Wiener filtering in DWT domain ⋮ A new nonlinear filter constructed from the Newton method and EPR in image restoration ⋮ An L1 Legendre-Galerkin spectral method with fast algorithm for the two-dimensional nonlinear coupled time fractional Schrödinger equation and its parameter estimation ⋮ An improved iteration regularization method and application to reconstruction of dynamic loads on a plate ⋮ Spectral method and Bayesian parameter estimation for the space fractional coupled nonlinear Schrödinger equations

Cites Work

• Nonlinear total variation based noise removal algorithms
• An algorithm for total variation minimization and applications
• A variational approach to remove outliers and impulse noise
• Optimization theory and methods. Nonlinear programming
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Numerical Optimization
• Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement
• Iterative Methods for Total Variation Denoising
• Scale Recognition, Regularization Parameter Selection, and Meyer's G Norm in Total Variation Regularization

This page was built for publication: A new method for parameter estimation of edge-preserving regularization in image restoration