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Proceedings Paper

Regularization methods for image restoration based on autocorrelation functions
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Paper Abstract

Image restoration is a procedure which is characterized by ill- poseness, ill-conditioning and non-uniqueness of the solution in the presence of noise. Iterative numerical methods have gained much attention for solving these inverse problems. Among the methods, minimal variance or least squares approaches are widely used and often generate good results at a reasonable cost in computing time using iterative optimization of the associated cost functional. In this paper, a new regularization method obtained by minimizing the autocorrelation function of residuals is proposed. Several numerical tests using the BFGS nonlinear optimization method are reported and comparisons to the classical Tikhonov regularization method are given. The results show that this method gives competitive restoration and is not sensitive to the regularization weighting parameter. Furthermore, a comprehensive procedure of image restoration is proposed by introducing a modified version of the Mumford-Shah model, which is often used in image segmentation. This approach shows promising improvement in restoration quality.

Paper Details

Date Published: 13 November 2000
PDF: 10 pages
Proc. SPIE 4116, Advanced Signal Processing Algorithms, Architectures, and Implementations X, (13 November 2000); doi: 10.1117/12.406516
Show Author Affiliations
Zhiping Mu, Wake Forest Univ. (United States)
Robert J. Plemmons, Wake Forest Univ. (United States)

Published in SPIE Proceedings Vol. 4116:
Advanced Signal Processing Algorithms, Architectures, and Implementations X
Franklin T. Luk, Editor(s)

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