Edge detection from noisy images using a neural edge detector

Abstract

In this paper, a new edge detector using a multilayer neural network, called a neural edge detector (NED), is proposed for detecting the desired edges clearly from noisy images. The NED is a supervised edge detector: through training the NED with a set of input images and desired edges, it acquires the function of a desired edge detector. The experiments on the NED to detect the edges from noisy test images and noisy natural images were performed. By comparative evaluation with the conventional edge detectors, the following has been demonstrated: the NED is robust against noise; the NED can detect clear continuous edges from the noisy images; and the performance of the NED is the highest in terms of similarity to the desired edges.