A new approach to constrained total least squares image restoration

Kwok Po NG, Robert J. Plemmons, Felipe Pimentel

Research output: Contribution to journalJournal articlepeer-review

31 Citations (Scopus)


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 languageEnglish
Pages (from-to)237-258
Number of pages22
JournalLinear Algebra and Its Applications
Issue number1-3
Publication statusPublished - 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


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