The Applicability of Neural Networks to Non-linear Image Processing

Abstract:

In this paper, the applicability of neural networks to non-linear image processing problems is studied. As an example, the Kuwahara filtering for edge-preserving smoothing was chosen. This filter is interesting due to its non-linear nature and natural modularity. A number of modular networks were constructed and trained, incorporating prior knowledge in various degrees and their performance was compared to standard feed-forward neural networks (MLPs). Based on results obtained in these experiments, it is shown that several key factors influence neural network behaviour in this kind of task. First, it is demonstrated that the mean squared error criterion used in neural network training is not representative for the problem. To be able to discern performance differences better, a new error measure for edge-preserving smoothing operations is proposed. Secondly, using this measure, it is shown that modular networks perform better than standard feed-forward networks. The latter type often ends up in linear approximations to the filter. Finally, inspection of the modular networks shows that, although analysis is difficult due to their non-linearity, one can draw some conclusions regarding the effect of design and training choices. The main conclusion is that neural networks can be applied to non-linear image processing problems, provided that careful attention is paid to network architecture, training set sampling and parameter choice. Only if prior knowledge is used in constructing the networks and sampling the datasets can one expect to obtain a well performing neural network filter.