Evolving military-grade image transforms using state-of-the-art variation operators

Michael R. Peterson; Gary B. Lamont; Frank Moore; Patrick Marshall

doi:10.1117/12.720920

2 May 2007 Evolving military-grade image transforms using state-of-the-art variation operators

Michael R. Peterson, Gary B. Lamont, Frank Moore, Patrick Marshall

Proceedings Volume 6563, Evolutionary and Bio-inspired Computation: Theory and Applications; 65630H (2007) https://doi.org/10.1117/12.720920
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Military imaging systems often require the transmission of copious amounts of data in noisy or bandwidth-limited situations. High rates of lossy image compression may be achieved through the use of quantization at the expense of resulting image quality. We employ genetic algorithms (GAs) to evolve military-grade transforms capable of improving reconstruction of satellite reconnaissance images under conditions subject to high quantization error. The resulting transforms outperform existing wavelet transforms at a given compression ratio allowing transmission of data at a lower bandwidth. Because GAs are notoriously difficult to tune, the selection of appropriate variation operators is critical when designing GAs for military-grade algorithm development. We test several state-of-the-art real-coded crossover and mutation operators to develop an evolutionary system capable of producing transforms providing robust performance over a set of fifty satellite images of military interest. With appropriate operators, evolved filters consistently provide an average mean squared error (MSE) reduction greater than 17% over the original wavelet transform. By improving image quality, evolved transforms increase the amount of intelligence that may be obtained reconstructed images.

Citation Download Citation

Michael R. Peterson, Gary B. Lamont, Frank Moore, and Patrick Marshall "Evolving military-grade image transforms using state-of-the-art variation operators", Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 65630H (2 May 2007); https://doi.org/10.1117/12.720920

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