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Proceedings Paper

Neural network approach to edge detection and noise reduction in low-contrast images
Author(s): Christopher M. Johnson; Edward W. Page; Gene A. Tagliarini
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Paper Abstract

Numerous vision applications rely upon efficient techniques for detecting edges in an image. Edge detection is especially difficult in low-contrast images which are characterized by the general lack of sharp variations in the gray-scale intensity values between objects of interest and their backgrounds. In low-contrast images, the application of commonly employed edge detection algorithms may result in excessive noise. This paper presents a neural network model which enhances edges and reduces noise in low-contrast gray-scale images. A neural element is associated with each pixel in an image. Each neuron receives weighted gray-scale inputs from its immediate neighbors. The weights associated with the gray-scale inputs are determined through a fuzzy compatibility function that grades the degree of similarity between the gray-scale intensity values of neighboring pixels. The neural element sums its weighted inputs and subjects the weighted sum to a sigmoid function that produces gray-scale outputs ranging between 0 and 255. The slope of the sigmoid function is chosen to force resulting pixel values away from mid-range values and closer to either 0 or 255. The resulting image is then subjected to the Sobel edge detection algorithm. The technique is illustrated by applying it to several low-contrast infrared images containing military vehicles. The results show significant noise reduction and edge enhancement.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205125
Show Author Affiliations
Christopher M. Johnson, Clemson Univ. (United States)
Edward W. Page, Clemson Univ. (United States)
Gene A. Tagliarini, Clemson Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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