A spatial summation model for image processing by artificial neural networks

The representation of the data used in a computational algorithm should be coupled to the computing machine on which the algorithm is implemented. A model for representing image data for gray-level image processing by artificial neural networks is presented. An input layer converts an image with real-valued or quantized intensities to a binary representation. The input layer uses threshold logic as the activation rule. An image processing task is decomposed into a collection of massively parallel decision tasks, each of which is performed locally by a hidden processing unit. The output of the hidden layer, in binary form, can be converted to a resultant image with real-valued intensities by the output layer. An advantage of this approach is that it is well-matched to the decision and classification capabilities of neural networks. A noise-suppressing algorithm with robust performance is implemented to illustrate the efficacy of this model