A suited approach to calculate the fractal dimension of images comes from power spectra of fractional Brownian motions: the ratio between powers at different scales is related to the persistence parameter II and, thus, to the fractal dimension D =3 - H. The signal-dependent nature of speckle noise, however, prevents from a correct estimation of fractal dimension from Synthetic Aperture Radar (SAR) images. Here, we propose and assess a novel method to obtain D based on the multi-scale decomposition provided by the normalized Lapla-cian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and designed to yield noise that is signal-independent. Experiments on both synthetic and true SAR images corroborate the underlying assumptions. © 1999 IEEE.
Aiazzi, B., Alparone, L., Bulletti, A., Baronti, S., Garzelli, A. (1999). Robust estimation of image fractal dimension based on pyramidal decomposition. In Proceedings of ICECS '99 (pp.533-536). IEEE [10.1109/ICECS.1999.812345].
Robust estimation of image fractal dimension based on pyramidal decomposition
GARZELLI A.
1999-01-01
Abstract
A suited approach to calculate the fractal dimension of images comes from power spectra of fractional Brownian motions: the ratio between powers at different scales is related to the persistence parameter II and, thus, to the fractal dimension D =3 - H. The signal-dependent nature of speckle noise, however, prevents from a correct estimation of fractal dimension from Synthetic Aperture Radar (SAR) images. Here, we propose and assess a novel method to obtain D based on the multi-scale decomposition provided by the normalized Lapla-cian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and designed to yield noise that is signal-independent. Experiments on both synthetic and true SAR images corroborate the underlying assumptions. © 1999 IEEE.
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https://hdl.handle.net/11365/5160
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