Intelligent edge enhancement using multilayer neural network based on multi-valued neurons
Igor N. Aizenberg, Shane Alexander, Jacob Jackson, Thomas Neal, Jeffrey Wilson, Kristi Kendrick · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
In this paper, we solve the edge enhancement problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent edge enhancer. The problem of neural edge enhancement using a classical multilayer feedforward neural network (MLF) was already considered by some authors. Since MLMVN significantly outperforms MLF in terms of learning speed, number of parameters employed, and generalization capability, it is very attractive to apply it for solving the edge enhancement problem. The main result which is presented in the paper, is the proven ability of MLMVN to enhance edges corresponding to a certain edge detection operator. Moreover, it is possible to enhance edges on noisy images ignoring a noisy texture. It is shown that to learn any edge detection operator using MLMVN, only a single image is required for learning purposes. The most important conclusion is that a neural network can learn different edge detection operators from a single example and then process those images that did not participate in the learning process detecting edges specifically corresponding to the learned operator with a high accuracy.