Automatic design of nonlinear filters by nearest neighbor learning

Abstract

Nonlinear filters have been used not only in noise elimination but in a wide variety of image processing applications. Traditionally the design of a digital filter is a manual task and the user accomplishes it based on previous experiences. Unfortunately often this is not a trivial task. Thus some works try to overcome this difficulty constructing the filters automatically by computational learning neural networks, genetic algorithms and statistical estimation. These works use typical input-output images of the application as the training samples. Many different kinds of filters can be easily constructed using this approach. This paper proposes the use of nearest neighbor (NN) learning to automatic filter construction. The kd-tree (k-dimensional binary tree) is used to accelerate the NN searching. A texture recognition application example is depicted.