Regularized constrained total least-squares image restoration

Vladimir Z. Mesarovic*, Nikolas P. Galatsanos, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In this paper the problem of restoring an image distorted by a linear space-invariant point- spread function (psf) which is not exactly known is formulated as the solution of a perturbed set of linear equations. The regularized constrained total least-squares (RCTLS) method is used to solve this set of equations. Using the diagonalization properties of the discrete Fourier transform (DFT) for circulant matrices, the RCTLS estimate is computed in the DFT domain. This significantly reduces the computational cost of this approach and makes its implementation possible for large images. Numerical experiments for different psf approximations are performed to check the effectiveness of the RCTLS approach for this problem. Objective and subjective comparisons are presented with the linear minimum mean-squared-error and the regularized least-squares estimates, for 2D images, that verify the superiority of the RCTLS approach.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages12
Editionp 2
ISBN (Print)081941638X
StatePublished - Dec 1 1994
EventVisual Communications and Image Processing '94 - Chicago, IL, USA
Duration: Sep 25 1994Sep 29 1994


OtherVisual Communications and Image Processing '94
CityChicago, IL, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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