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Proceedings Paper

Edge-preserving image restoration based on robust stabilizing functionals
Author(s): Michael E. Zervakis
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Paper Abstract

The regularization of the least-squares criterion has been established as an effective approach in linear image restoration. However, the quadratic smoothing functions employed in this approach degrade the detailed structure of the estimate. This paper introduces the concept of robust estimation in regularized image restoration, and addresses its potential in preserving the detailed structure. Robust estimation schemes have been used in the suppression of artifacts created by long-tailed noise processes. In this paper it is demonstrated that a robust objective function allows the existence of sharp signal transitions in the estimate, by alleviating the penalty on the error associated with such transitions. The optimization approach introduced modifies the stabilizing term of the regularized criterion according to the notion of M- estimation. Thus, an influence function is employed to restrain the contribution of large estimate-deviations in the optimization criterion. Moreover, the utilization of a new entropic criterion as the stabilizing functional is explored. The general structure of this criterion shares many common characteristics with the functions employed in robust M-estimation. The robust criteria provide nonlinear estimation schemes which efficiently preserve the detailed structure, even in the case of an over-estimated regularization parameter.

Paper Details

Date Published: 19 May 1992
PDF: 12 pages
Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58325
Show Author Affiliations
Michael E. Zervakis, Univ. of Minnesota/Duluth (United States)


Published in SPIE Proceedings Vol. 1657:
Image Processing Algorithms and Techniques III
James R. Sullivan; Benjamin M. Dawson; Majid Rabbani, Editor(s)

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