Application of an adaptive plan to the configuration of nonlinear image-processing algorithms

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Abstract
The application of an adaptive plan to the design of a class of nonlinear digital image processing operators known as stack filters is presented in this paper. The adaptive plan is based on the mechanics found in genetics and natural selection. Such learning mechanisms have become known as genetic algorithms. A stack filter is characterized by the coefficients of its underlying positive Boolean function. This set of coefficients constitute a binary string, referred to as a chromosome in a genetic algorithm, that represents that particular filter configuration. A fitness value for each chromosome is computed based on the performance of the associated filter in specific tasks such as noise suppression. A population of chromosomes is maintained by the genetic algorithm, and new generations are formed by selecting mating pairs based on their fitness values. Genetic operators such as crossover or mutation are applied to the mating pairs to form offsprings. By exchanging some substrings of the two parent-chromosomes, the crossover operator can bring different blocks of genes that result in good performance together into one chromosome that yields the best performance. Empirical results show that this method is capable of configuring stack filters that are effective in impulsive noise suppression.

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