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Proceedings Paper

Pattern theoretic image restoration
Author(s): Michael A. Breen; Timothy D. Ross; Michael J. Noviskey; Mark L. Axtell
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Paper Abstract

Pattern theory is a combination of pattern recognition, machine learning, switching theory, and computational complexity technologies with the central theme that the pattern in a function can be found by minimizing the complexity of a particular generalized representation. The sense of `pattern' used in pattern theory has been demonstrated to be very robust. This paper develops a pattern theoretic approach to image restoration. We assume that an original, patterned, binary image has been corrupted by additive noise and is given as a gray-scale image. The decision theoretic approach to restoration would be simply to threshold the gray- scale image to regain a binary image. The pattern theoretic approach is to use two thresholds. These thresholds separate the pixels into three classes: pixels that were very probably white, pixels that were very probably black, and pixels that we are less certain about. We then use only those pixels that we are confident about and find the pattern based on those pixels. Finally, we use this pattern to extrapolate through the pixels that are uncertain. The amount of noise that can be abated depends on the strength of the underlying pattern. This relationship is developed for uniform and normal noise distributions.

Paper Details

Date Published: 21 May 1993
PDF: 11 pages
Proc. SPIE 1902, Nonlinear Image Processing IV, (21 May 1993); doi: 10.1117/12.144753
Show Author Affiliations
Michael A. Breen, Tennessee Technological Univ. (United States)
Timothy D. Ross, Air Force Wright Lab. (United States)
Michael J. Noviskey, Air Force Wright Lab. (United States)
Mark L. Axtell, Veda Inc. (United States)

Published in SPIE Proceedings Vol. 1902:
Nonlinear Image Processing IV
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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