Circulant preconditioners for discrete ill-posed Toeplitz systems

TL;DR

This paper develops circulant block Toeplitz preconditioners for symmetric ill-posed linear systems, especially in image deblurring, ensuring stability and efficiency in the presence of data errors.

Contribution

It introduces a perturbation-based approach to select preconditioners that preserve critical eigen-subspaces in symmetric block Toeplitz systems from ill-posed problems.

Findings

Preconditioners improve convergence in image deblurring.

The method maintains stability despite data errors.

Application demonstrated on real image restoration tasks.

Abstract

Circulant preconditioners are commonly used to accelerate the rate of convergence of iterative methods when solving linear systems of equations with a Toeplitz matrix. Block extensions that can be applied when the system has a block Toeplitz matrix with Toeplitz blocks also have been developed. This paper is concerned with preconditioning of linear systems of equations with a symmetric block Toeplitz matrix with symmetric Toeplitz blocks that stem from the discretization of a linear ill-posed problem. The right-hand side of the linear systems represents available data and is assumed to be contaminated by error. These kinds of linear systems arise, e.g., in image deblurring problems. It is important that the preconditioner does not affect the invariant subspace associated with the smallest eigenvalues of the block Toeplitz matrix to avoid severe propagation of the error in the right-hand side. A perturbation result indicates how the dimension of the subspace associated with the smallest eigenvalues should be chosen and allows the determination of a suitable preconditioner when an estimate of the error in the right-hand side is available. This estimate also is used to decide how many iterations to carry out by a minimum residual iterative method. Applications to image restoration are presented.