Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm

DOI10.1137/S0895479801395446zbMath1036.65036OpenAlexW2139982805MaRDI QIDQ4443787

Armin Pruessner, Dianne P. O'Leary

Publication date: 18 January 2004

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/s0895479801395446

zbMATH Keywords

algorithmregularizationill-posed problemblind deconvolutionimage deblurring2-normoverdetermined linear system1-normconstrained total least squaresstructured total least norm\(\infty\)-norm

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Ill-posedness and regularization problems in numerical linear algebra (65F22)

Related Items (18)

Polynomial computations for blind image deconvolution ⋮ A regularized structured total least squares algorithm for high-resolution image reconstruction ⋮ Implementation of the regularized structured total least squares algorithms for blind image deblurring ⋮ Total least-squares regularization of Tykhonov type and an ancient racetrack in Corinth ⋮ A parameter estimation method for model analysis ⋮ Constrained numerical optimization methods for blind deconvolution ⋮ Nested alternating minimization with FISTA for non-convex and non-smooth optimization problems ⋮ Stochastic algorithms for solving structured low-rank matrix approximation problems ⋮ Fast deconvolution with approximated PSF by RSTLS with antireflective boundary conditions ⋮ Optimization challenges in the structured low rank approximation problem ⋮ Regularization methods for uniformly rank-deficient nonlinear least-squares problems ⋮ A Kronecker approximation with a convex constrained optimization method for blind image restoration ⋮ On the stability of the risk hull method for projection estimators ⋮ An Alternating Semiproximal Method for Nonconvex Regularized Structured Total Least Squares Problems ⋮ Fast robust regression algorithms for problems with Toeplitz structure ⋮ Randomized core reduction for discrete ill-posed problem ⋮ Regularized total least squares based on quadratic eigenvalue problem solvers ⋮ A method for structured linear total least norm on blind deconvolution problem

Uses Software

• LIPSOL

This page was built for publication: Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm