Evolving matched filter transform pairs for satellite image processing

Michael R. Peterson; Toby Horner; Frank Moore

doi:10.1117/12.884312

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19 May 2011 Evolving matched filter transform pairs for satellite image processing

Michael R. Peterson, Toby Horner, Frank Moore

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Proceedings Volume 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V; 80590L (2011) https://doi.org/10.1117/12.884312
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Wavelets provide an attractive method for efficient image compression. For transmission across noisy or bandwidth limited channels, a signal may be subjected to quantization in which the signal is transcribed onto a reduced alphabet in order to save bandwidth. Unfortunately, the performance of the discrete wavelet transform (DWT) degrades at increasing levels of quantization. In recent years, evolutionary algorithms (EAs) have been employed to optimize wavelet-inspired transform filters to improve compression performance in the presence of quantization. Wavelet filters consist of a pair of real-valued coefficient sets; one set represents the compression filter while the other set defines the image reconstruction filter. The reconstruction filter is defined as the biorthogonal inverse of the compression filter. Previous research focused upon two approaches to filter optimization. In one approach, the original wavelet filter is used for image compression while the reconstruction filter is evolved by an EA. In the second approach, both the compression and reconstruction filters are evolved. In both cases, the filters are not biorthogonally related to one another. We propose a novel approach to filter evolution. The EA optimizes a compression filter. Rather than using a wavelet filter or evolving a second filter for reconstruction, the reconstruction filter is computed as the biorthogonal inverse of the evolved compression filter. The resulting filter pair retains some of the mathematical properties of wavelets. This paper compares this new approach to existing filter optimization approaches to determine its suitability for the optimization of image filters appropriate for defense applications of image processing.

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Michael R. Peterson, Toby Horner, and Frank Moore "Evolving matched filter transform pairs for satellite image processing", Proc. SPIE 8059, Evolutionary and Bio-Inspired Computation: Theory and Applications V, 80590L (19 May 2011); https://doi.org/10.1117/12.884312

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