Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
SIAM Journal on Matrix Analysis and Applications
Decomposition methods for large linear discrete ill-posed problems
Journal of Computational and Applied Mathematics - Special issue: Applied computational inverse problems
A Projection-Based Approach to General-Form Tikhonov Regularization
SIAM Journal on Scientific Computing
Arnoldi-Tikhonov regularization methods
Journal of Computational and Applied Mathematics
Invertible smoothing preconditioners for linear discrete ill-posed problems
Applied Numerical Mathematics - 6th IMACS International symposium on iterative methods in scientific computing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Alternating Krylov subspace image restoration methods
Journal of Computational and Applied Mathematics
A generalized global Arnoldi method for ill-posed matrix equations
Journal of Computational and Applied Mathematics
An adaptive norm algorithm for image restoration
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Subspace echo state network for multivariate time series prediction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Automatic parameter setting for Arnoldi-Tikhonov methods
Journal of Computational and Applied Mathematics
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We consider Tikhonov regularization of large linear discrete ill-posed problems with a regularization operator of general form and present an iterative scheme based on a generalized Krylov subspace method. This method simultaneously reduces both the matrix of the linear discrete ill-posed problem and the regularization operator. The reduced problem so obtained may be solved, e.g., with the aid of the singular value decomposition. Also, Tikhonov regularization with several regularization operators is discussed.