Iterative evaluation of the regularization parameter in regularized image restoration

Authors:
A. K. Katsaggelos;M. G. Kang
Affiliations:
Department of Electrical Engineering and Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, 60208-3118, USA;Department of Electrical Engineering and Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, 60208-3118, USA
Venue:
Journal of Visual Communication and Image Representation
Year:
1992

Citing 4
Cited 1

The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer

IEEE Transactions on Computers
A regularized iterative image restoration algorithm

IEEE Transactions on Signal Processing
Simultaneous iterative image restoration and evaluation of theregularization parameter

IEEE Transactions on Signal Processing
Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation

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An adaptive algorithm for image restoration using combined penalty functions

Pattern Recognition Letters

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Abstract

In this paper a nonlinear regularized iterative image restoration algorithm is proposed, according to which no prior knowledge about the noise variance is assumed. The algorithm results from a set-theoretic regularization approach, where bounds of the stabilizing functional and the noise variance, which determine the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm, as well as an optimality criterion for the regularization parameter, are derived and experimental results are shown.