Comments on ‘‘Modeling fractional stochastic systems as non-random fractional dynamics driven Brownian motions

Applied Mathematical Modelling, Vol.33Some results presented in the paper ‘‘Modeling fractional stochastic systems as nonrandom fractional dynamics driven Brownian motions” [I. Podlubny, Fractional Differential Equations, Academic Press, San Diego, 1999] are discussed in this paper. The slightly modified Grünwald-Letnikov derivative proposed there is used to deduce some interesting results that are in contradiction with those proposed in the referred paper

Analyse de champs browniens fractionnaires anisotropes

Cette communication présente la Méthode des Moyennes Directionnelles (MMD) pour analyser un Champ Brownien Fractionnaire Anisotrope (CBFA) défini dans le domaine spectral par un paramètre de Hurst H(θ) variable suivant la direction θ. Pour une direction φ donnée sur l'image, on calcule la moyenne de lignes dans cette direction ainsi que la régularité R(φ) du signal résultant. Nous montrons que pour θ = (p + π/2, la fonction H(θ) vaut R(φ) - 1/2 et peut-être ainsi estimée. Nous avons testé cette méthode sur des images type fractales isotropes et sur des CBFA synthétisés par la méthode de Fourier inverse. Les résultats apparaissent tout à fait corrects malgré les difficultés de synthèse précise de CBFA, de l'analyse de signaux dont l'exposant de Hôlder dépasse 1, et de la discrétisation des images. Ces travaux ouvrent des perspectives intéressantes concernant le calcul possible des paramètres d'une structure fractale poreuse 3D connue par sa projection comme c'est le cas pour une radiographie

Self-similar prior and wavelet bases for hidden incompressible turbulent motion

This work is concerned with the ill-posed inverse problem of estimating turbulent flows from the observation of an image sequence. From a Bayesian perspective, a divergence-free isotropic fractional Brownian motion (fBm) is chosen as a prior model for instantaneous turbulent velocity fields. This self-similar prior characterizes accurately second-order statistics of velocity fields in incompressible isotropic turbulence. Nevertheless, the associated maximum a posteriori involves a fractional Laplacian operator which is delicate to implement in practice. To deal with this issue, we propose to decompose the divergent-free fBm on well-chosen wavelet bases. As a first alternative, we propose to design wavelets as whitening filters. We show that these filters are fractional Laplacian wavelets composed with the Leray projector. As a second alternative, we use a divergence-free wavelet basis, which takes implicitly into account the incompressibility constraint arising from physics. Although the latter decomposition involves correlated wavelet coefficients, we are able to handle this dependence in practice. Based on these two wavelet decompositions, we finally provide effective and efficient algorithms to approach the maximum a posteriori. An intensive numerical evaluation proves the relevance of the proposed wavelet-based self-similar priors.Comment: SIAM Journal on Imaging Sciences, 201

Effective Wavelet-Based Regularization of Divergence-free Fractional Brownian Motion

This paper presents a method for regularization of inverse problems. The vectorial bi-dimensional unknown is assumed to be the realization of an isotropic divergence-free fractional Brownian Motion (fBm). The method is based on fractional Laplacian and divergence-free wavelet bases. The main advantage of these bases is to enable an easy formalization in a Bayesian framework of fBm priors, by simply sampling wavelet coe cients according to Gaussian white noise. Fractional Laplacians and the divergence-free projector can naturally be implemented in the Fourier domain. An interesting alternative is to remain in the spatial domain. This is achieved by the analytical computation of the connection coefficients of divergence-free fractional Laplacian wavelets, which enables to easily rotate this simple prior in any sufficiently "regular" wavelet basis. Taking advantage of the tensorial structure of a separable fractional wavelet basis approximation, isotropic regularization is then computed in the spatial domain by low-dimensional matrix products. The method is successfully applied to fractal image restoration and turbulent optic-flow estimation