Abstract
Recently there has been a growing interest and progress in using total least squares (TLS) methods for solving blind deconvolution problems arising in image restoration. Here, the true image is to be estimated using only partial information about the blurring operator, or point spread function (PSF), which is subject to error and noise. In this paper, we present a new iterative, regularized, and constrained TLS image restoration algorithm. Neumann boundary conditions are used to reduce the boundary artifacts that normally occur in restoration processes. Preliminary numerical tests are reported on some simulated optical imaging problems in order to illustrate the effectiveness of the approach, as well as the fast convergence of our iterative scheme.
Original language | English |
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Pages (from-to) | 237-258 |
Number of pages | 22 |
Journal | Linear Algebra and Its Applications |
Volume | 316 |
Issue number | 1-3 |
DOIs | |
Publication status | Published - 1 Sept 2000 |
Scopus Subject Areas
- Algebra and Number Theory
- Numerical Analysis
- Geometry and Topology
- Discrete Mathematics and Combinatorics
User-Defined Keywords
- Constrained total least squares
- Deconvolution
- Neumann boundary condition
- Regularization
- Toeplitz matrix