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Proceedings Paper

LUM filters for smoothing and sharpening
Author(s): Charles G. Boncelet Jr.; Russell C. Hardie; M. Reza Hakami; Gonzalo R. Arce
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Paper Abstract

We introduce the LUM filter for both smoothing and sharpening. The LUM filter is a moving window estimator that does the following: it finds the order statistics by sorting the samples in the window, and it compares a lower order statistic, an upper order statistic, and the middle sample. The two order statistics define a range of 'normal' values. If smoothing is desired, the LUM filter outputs the middle sample if it is between the two order statistics; otherwise, it outputs the closest of the two order statistics. If sharpening is desired, the roles are reversed. The LUM sharpener outputs the middle sample if it is outside the two order statistics; otherwise it outputs the closest of the two order statistics. Furthermore, both characteristics can be achieved at the same time. We compare the LUM filter against common alternatives such as linear smoothers and sharpeners, moving medians, and sharpeners such as the CS filter. In summary, we believe the LUM filter is widely applicable and has good performance in a wide range of applications.

Paper Details

Date Published: 1 April 1991
PDF: 5 pages
Proc. SPIE 1451, Nonlinear Image Processing II, (1 April 1991); doi: 10.1117/12.44317
Show Author Affiliations
Charles G. Boncelet Jr., Univ. of Delaware (United States)
Russell C. Hardie, Univ. of Delaware (United States)
M. Reza Hakami, Univ. of Delaware (United States)
Gonzalo R. Arce, Univ. of Delaware (United States)

Published in SPIE Proceedings Vol. 1451:
Nonlinear Image Processing II
Edward R. Dougherty; Gonzalo R. Arce; Charles G. Boncelet Jr., Editor(s)

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