How do neural networks compare with standard filters for image noise suppression?

The present paper has been partly motivated by curiosity-can ANNs successfully cope with image noise removal? If so, can they improve on recognised noise suppression techniques? One must remember that the latter use conventional algorithms, or the corresponding hardware, and are not trained to perform the task. Yet the very fact of training reflects that ANNs learn by example, embodying implicit learning rules-thereby emulating biological systems and providing the potential to improve on conventional algorithmic approaches. In fact, there are possibilities that ANNs might perform noise suppression more effectively than conventional approaches, not least in adapting to specific types of noise, and in eliminating the image distortion which is a characteristic of the widely used median filter. In this context it is worth noting that the median filter has no adjustable parameters other than neighbourhood size, so ANNs definitely have the potential for improving on its performance-and also on that of alternative types of filter. The paper describes the authors´ own studies of the problem