Efficiently determining transform filter coefficients for image processing by applying distributed genetic algorithms

Martin Gilligan; Gary B Lamont; Michael R. Peterson

doi:10.1117/12.822215

29 April 2009 Efficiently determining transform filter coefficients for image processing by applying distributed genetic algorithms

Martin Gilligan, Gary B Lamont, Michael R. Peterson

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Proceedings Volume 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III; 73470E (2009) https://doi.org/10.1117/12.822215
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

An important aspect of contemporary military communications in the design of robust image transforms for defense surveillance applications. In particular, efficient yet effective transfer of critical image information is required for decision making. The generic use of wavelets to transform an image is a standard transform approach. However, the resulting bandwidth requirements can be quite high, suggesting that a different bandwidth-limited transform be developed. Thus, our specific use of genetic algorithms (GAs) attempts to replace standard wavelet filter coefficients with an optimized transform filter in order to retain or improve image quality for bandwidth-restricted surveillance applications. To find improved coefficients efficiently, we have developed a software engineered distributed design employing a genetic algorithm (GA) parallel island model on small and large computational clusters with multi-core nodes. The main objective is to determine whether running a distributed GA with multiple islands would either give statistically equivalent results quicker or obtain better results in the same amount of time. In order to compare computational performance with our previous serial results, we evaluate the obtained "optimal" wavelet coefficients on test images from both approaches which results in excellent comparative metric values.

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Martin Gilligan, Gary B Lamont, and Michael R. Peterson "Efficiently determining transform filter coefficients for image processing by applying distributed genetic algorithms", Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 73470E (29 April 2009); https://doi.org/10.1117/12.822215

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KEYWORDS

Image filtering

Wavelets

Genetic algorithms

Surveillance

Image processing

Image quality standards

Algorithm development

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