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Multiresolution estimation of fractal dimension from images affected by signal-dependent noise (Contributo in atti di convegno)

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Multiresolution estimation of fractal dimension from images affected by signal-dependent noise (Contributo in atti di convegno) (literal)

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Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Andrea Garzelli (1999)
Multiresolution estimation of fractal dimension from images affected by signal-dependent noise
in SPIE Annual Meeting 1999 (44th SPIE Annual Meeting): Wavelet Applications in Signal and Image Processing VII, Denver, CO, USA, 19-23 Luglio 1999
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Proceedings of the 44th SPIE Annual Meeting: Wavelet Applications in Signal and Image Processing VII (literal)

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\"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy Department of Electronic Engineering, University of Florence, Via S. Marta, 3, I-50139 Firenze, Italy \"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy Department of Information Engineering, University of Siena, Via Roma, 56, I-53100 Siena, Italy (literal)

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Multiresolution estimation of fractal dimension from images affected by signal-dependent noise (literal)

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Abstract

A well-suited approach to calculate the fractal dimension of digital images stems from the power spectrum of a fractional Brownian motion: the ratio between powers at different scales is related to the persistence parameter H and, thus, to the fractal dimension D = 3 - H. The signal-dependent nature of the 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 multiscale decomposition provided by the normalized Laplacian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and is designed to force the noise to become signal-independent. Extensive experiments on synthetic fractal textures, both noise-free and noisy, corroborate the underlying assumptions and show the performances, in terms of both accuracy and confidence of estimation, of pyramid methods compared with the well-established method based on the wavelet transform. Preliminary results on true SAR images from ERS-1 look promising as well. (literal)

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