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Proceedings Paper

Performance of stack filters and vector detection in image restoration
Author(s): Kenneth E. Barner; Gonzalo R. Arce; Jean H. Lin
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Paper Abstract

Two techniques for image restoration are compared in this paper. One is a technique based on the theory of optimal adaptive stack filtering; the other is a recently developed vector detection approach to image restoration. The primary difference between these two techniques is that the optimal detection technique exploits multilevel a priori information, while the stack filter uses only single level information. Both approaches have very similar design constraints: (a) both rely on the existence of a training sequence for the image source in order to obtain optimal processing; (b) the underlying random fields need not be stationary and a direct computation of the statistics of the desired images is not required. Adaptive stack filters do, however, require a training set of the noise while the optimal detection approach only needs a multivariate parametric representation. The image restoration performance of these two methods is compared in a signal dependent noise environment characterizing imaging systems with speckle, film-grain, and Poisson shot noise. Comparisons are made using the Mean Absolute Error measure as well as a subjective measure.

Paper Details

Date Published: 1 July 1990
PDF: 12 pages
Proc. SPIE 1247, Nonlinear Image Processing, (1 July 1990); doi: 10.1117/12.19611
Show Author Affiliations
Kenneth E. Barner, Univ. of Delaware (United States)
Gonzalo R. Arce, Univ. of Delaware (United States)
Jean H. Lin, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 1247:
Nonlinear Image Processing
Edward J. Delp, Editor(s)

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