Avoiding the inverse fractal problem for compressive sampling of 1/f data sets

Holger Jaenisch

doi:10.1117/12.914805

10 May 2012 Avoiding the inverse fractal problem for compressive sampling of 1/f data sets

Holger Jaenisch

Author Affiliations +

Proceedings Volume 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X; 84010N (2012) https://doi.org/10.1117/12.914805
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

We present a novel fractal Iterated Function System (IFS based) data interpolation algorithm enabling compressive sampling of 1/f data sets. We avoid the classical inverse IFS parameter estimation problem by using a novel analytical function driven variant of the random Iterated Function System (IFS) algorithm. We attempt to optimize the parameters of the analytical driver equation to optimize the data reconstruction by minimizing errors using various state-of-the-art genetic algorithms. We demonstrate our encouraging results and detail our methods and findings.

Citation Download Citation

Holger Jaenisch "Avoiding the inverse fractal problem for compressive sampling of 1/f data sets", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010N (10 May 2012); https://doi.org/10.1117/12.914805

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