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Proceedings Paper

Robust regularized image restoration
Author(s): Taek-Mu Kwon; Michael E. Zervakis
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Paper Abstract

Many image processing applications involve long-tailed noise processes, which introduce outliers in the gray-level distribution of the image. The performance of conventional restoration algorithms is highly degraded by such noise processes. A novel restoration approach is introduced, which combines the properties of regularized and robust estimation schemes. Most prominent regularized approaches attempt to compensate for the ill-posedness of the pseudo-inverse solution. Regularization is achieved by constraining the least squares solution in terms of a smoothing criterion. The optimization approach introduced in this paper further modifies the regularized criterion according to the notion of M-estimation. Thus, an influence function is employed in restraining the contribution of large estimate-deviations in the optimization criterion. The modified criterion provides nonlinear estimates, which do not suffer from artifacts due to the presence of long-tailed noise. The computation of the robust regularized estimate is based on the simple structure of a steepest-descent iterative procedure. One of the most important factors associated with the concept of regularization is the regularization parameter. Adaptive schemes concerning the selection of this parameter at every iteration step are introduced. The convergence properties of the robust and the adaptive algorithms introduced are extensively studied. The capabilities of the robust regularized algorithms are demonstrated through restoration examples.

Paper Details

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48390
Show Author Affiliations
Taek-Mu Kwon, Univ. of Minnesota/Duluth (United States)
Michael E. Zervakis, Univ. of Minnesota/Duluth (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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