Paper
1 July 1990 Application of an adaptive plan to the configuration of nonlinear image-processing algorithms
Author Affiliations +
Proceedings Volume 1247, Nonlinear Image Processing; (1990) https://doi.org/10.1117/12.19614
Event: Electronic Imaging: Advanced Devices and Systems, 1990, Santa Clara, CA, United States
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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chee-Hung Henry Chu "Application of an adaptive plan to the configuration of nonlinear image-processing algorithms", Proc. SPIE 1247, Nonlinear Image Processing, (1 July 1990); https://doi.org/10.1117/12.19614
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Cited by 4 scholarly publications.
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KEYWORDS
Digital filtering

Binary data

Genetic algorithms

Genetics

Nonlinear image processing

Image processing

Interference (communication)

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