A method for structured linear total least norm on blind deconvolution problem

DOI10.1007/BF02935795zbMath1082.65044OpenAlexW2007363146MaRDI QIDQ2574332

Sunjoo Kwon, Jae Heon Yun, Se-Young Oh

Publication date: 21 November 2005

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

Full work available at URL: https://doi.org/10.1007/bf02935795

zbMATH Keywords

numerical examplesblock Toeplitz matriximage restorationblind deconvolutionoverdetermined linear systemregularized structured total least norm methodresidual reductionToeplitz block structure

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Polynomial computations for blind image deconvolution ⋮ A parameter estimation method for model analysis ⋮ On condition numbers for Moore–Penrose inverse and linear least squares problem involving Kronecker products

Uses Software

• RestoreTools

Cites Work

• An Analysis of the Total Least Squares Problem
• Deconvolution and regularization with Toeplitz matrices
• Iterative methods for image deblurring: A Matlab object-oriented approach
• Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
• Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm
• Total Least Norm Formulation and Solution for Structured Problems

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