Neural network approach to edge detection and noise reduction in low-contrast images

摘要

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 grey-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 grey-scale images. A neural element is associated with each pixel in an image. Each neuron receives weighted grey-scale inputs from its immediate neighbors. The weights associated with the grey-scale inputs are determined through a fuzzy compatibility function that grades the degree of similarity between the grey-scale intensity values of neighboring pixels. The neural element sums its weighted inputs and subjects the weighted sum to a sigmoid function that produces grey-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.