Approximate Schur Complement Preconditioners for Half‐Quadratic Image Restoration With Zero Boundary Conditions

In this paper, the additive half‐quadratic image restoration problem with zero boundary conditions is investigated. The Newton method is used to solve this problem and a structured linear system needs solving at each step. In order to accelerate this process, we have proposed a preconditioning method based on approximations of the Schur complement and the blurring matrix. The block Toeplitz matrix is approximated as a block circulant matrix and the fast Fourier transform is used to implement matrix–vector multiplications. We give an analysis of the eigenvalue property of the preconditioned Hessian matrix. Numerical results demonstrate the effectiveness of the proposed preconditioning method.

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Publication Details

Journal
Journal of Mathematics
Published
2025-01-01
DOI
https://doi.org/10.1155/jom/9935466
Citations
1
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

Approximate Schur Complement Preconditioners for Half‐Quadratic Image Restoration With Zero Boundary Conditions

Chaojie Wang, Biling Liu, Jie Chen, Shuen Sun
1 citations
Journal of Mathematics
Sparse and Compressive Sensing Techniques
1.33
article

Approximate Schur Complement Preconditioners for Half‐Quadratic Image Restoration With Zero Boundary Conditions

Chaojie Wang, Biling Liu, Jie Chen, Shuen Sun
article en
1 citations

Abstract

In this paper, the additive half‐quadratic image restoration problem with zero boundary conditions is investigated. The Newton method is used to solve this problem and a structured linear system needs solving at each step. In order to accelerate this process, we have proposed a preconditioning method based on approximations of the Schur complement and the blurring matrix. The block Toeplitz matrix is approximated as a block circulant matrix and the fast Fourier transform is used to implement matrix–vector multiplications. We give an analysis of the eigenvalue property of the preconditioned Hessian matrix. Numerical results demonstrate the effectiveness of the proposed preconditioning method.

Journal of MathematicsVol. 2025(1)
Beijing Technology and Business University (CN)
National Natural Science Foundation of China, Beijing Technology and Business University
Openalex Percentile: Top 25%
Sparse and Compressive Sensing Techniques
1.33
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