6 April 1995 Neural network approach to edge detection and noise reduction in low-contrast images
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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.
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Christopher M. Johnson, Edward W. Page, Gene A. Tagliarini, "Neural network approach to edge detection and noise reduction in low-contrast images", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205125; https://doi.org/10.1117/12.205125
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