# Wavelet-based multifractal analysis

Alain Arneodo et al. (2008), Scholarpedia, 3(3):4103. | doi:10.4249/scholarpedia.4103 | revision #137211 [link to/cite this article] |

The **multifractal formalism** was introduced in the context of fully-developed turbulence
data analysis and modeling to account for the experimental observation of some deviation
to Kolmogorov theory (K41) of homogenous and isotropic turbulence (Frisch, 1995). The
predictions of various multiplicative cascade models, including the weighted curdling (binomial)
model proposed by Mandelbrot (1974), were tested using box-counting (BC) estimates
of the so-called \(f(\alpha)\) singularity spectrum of the dissipation field (Meneveau & Sreenivasan,
1991). Alternatively, the intermittent nature of the velocity fluctuations were investigated
via the computation of the \(D(h)\) singularity spectrum using the structure function (SF)
method (Parisi & Frisch, 1985). Unfortunately, both types of studies suffered from severe insufficiencies.
On the one hand, they were mostly limited by one point probe measurements to
the analysis of one (longitudinal) velocity component and to some 1D surrogate approximation
of the dissipation (Aurell et al., 1992). On the other hand, both the BC and SF methodologies
have intrinsic limitations and fail to fully characterize the corresponding singularity spectrum
since only the strongest singularities are a priori amenable to these techniques (Arneodo et al.,
1995b; Bacry et al., 1993; Muzy et al., 1993, 1994). In the early nineties, a statistical approach
based on the **continuous wavelet transform** was proposed as a unified multifractal description
of singular measures and multi-affine functions (Arneodo et al., 1995b; Bacry et al., 1993;
Muzy et al., 1993, 1994). Applications of the so-called **wavelet transform modulus maxima
(WTMM)** method have already provided insight into a wide variety of problems, e.g., fully
developed turbulence, econophysics, meteorology, physiology and DNA sequences (Arneodo
et al., 2002). Let us note that alternative approaches to the multifractal description have
been developed using discrete wavelet bases (Abry et al., 2000, 2002a,b; Veitch & Abry, 1999)
including the recent use of wavelet leaders (Jaffard et al., 2006; Wendt & Abry, 2007; Wendt
et al., 2007). Later on, the WTMM method was generalized to 2D for multifractal analysis
of rough surfaces (Arneodo et al., 2000; Decoster et al., 2000), with very promising results
in the context of the geophysical study of the intermittent nature of satellite images of the
cloud structure (Arneodo et al., 1999a, 2003; Roux et al., 2000) and the medical assist in the
diagnosis in digitized mammograms (Arneodo et al., 2003; Kestener et al., 2001). Recently,
the WTMM method has been further extended to 3D scalar as well as 3D vector field analysis
and applied to 3D numerical data issue from isotropic turbulence direct numerical simulations
(DNS) (Kestener & Arneodo, 2003, 2004, 2007).

## Contents |

## The continuous wavelet transform

### Introduction

The continuous wavelet transform (WT) is a mathematical technique introduced in signal
analysis in the early 1980s (Goupillaud et al., 1984; Grossmann & Morlet, 1984).
Since then, it has been the subject of considerable theoretical developments
and practical applications in a wide variety of fields.
The WT has been early recognized as a mathematical microscope that
is well adapted to reveal the hierarchy that governs the spatial
distribution of singularities of multifractal
measures (Arneodo et al., 1988, 1989, 1992).
What makes the WT of fundamental use in the present study is that its
singularity scanning ability equally applies to singular functions
than to singular measures (Arneodo et al., 1988, 1989, 1992; Holschneider, 1988; Holschneider & Tchamitchian, 1990; Jaffard, 1989, 1991; Mallat & Hwang, 1992; Mallat
& Zhong, 1992).
This has led Alain Arneodo and his collaborators (Arneodo et al., 1995b; Bacry et al., 1993; Muzy et al., 1991, 1993, 1994)
to elaborate a unified thermodynamic description of multifractal
distributions including measures and functions, the so-called Wavelet
Transform Modulus Maxima (WTMM) method.
By using wavelets instead of boxes, one can take advantage of the
freedom in the choice of these "generalized oscillating boxes"
to get rid of possible (smooth) polynomial behavior that might
either mask singularities or perturb the estimation of their
strength \(h\) (Hölder exponent), remedying in this way for one
of the main failures of the classical multifractal methods
(*e.g.* the box-counting algorithms in the case of measures and the
structure function method in the case of functions (Arneodo et al., 1995b; Bacry et al., 1993; Muzy et al., 1993, 1994)).
The other fundamental advantage of using wavelets is that
the skeleton defined by the WTMM (Mallat & Hwang, 1992; Mallat & Zhong, 1992),
provides an adaptative space-scale partitioning from which one
can extract the \(D(h)\) singularity spectrum via the Legendre
transform of the scaling exponents \(\tau(q)\) (\(q\) real, positive
as well as negative) of some partition functions defined from the
WT skeleton.
We refer the reader to Bacry et al. (1993), Jaffard (1997a,b) for rigorous mathematical results
and to Hentschel (1994) for the theoretical treatment
of random multifractal functions.

### Definition

The WT is a space-scale analysis which consists in
expanding signals in terms of *wavelets* which are
constructed from a single function, the *analyzing wavelet*
\(\psi\ ,\) by means of translations and dilations.
The WT of a real-valued function \(f\) is defined
as (Goupillaud et al., 1984; Grossmann
& Morlet, 1984)\[\tag{1}
T_\psi[f](x_0,a)= \frac{1}{a}\int_{-\infty}^{+\infty} f(x)\psi(\frac{x-x_0}{a})dx \; ,
\]

where \(x_0\) is the space parameter and \(a\) (\(>0\)) the scale parameter. The analyzing wavelet \(\psi\) is generally chosen to be well localized in both space and frequency. Usually \(\psi\) is required to be of zero mean for the WT to be invertible. But for the particular purpose of singularity tracking that is of interest here, we will further require \(\psi\) to be orthogonal to low-order polynomials (Arneodo et al., 1995b; Bacry et al., 1993; Holschneider & Tchamitchian, 1990; Jaffard, 1989, 1991; Mallat & Hwang, 1992; Mallat & Zhong, 1992; Muzy et al., 1991, 1993, 1994):

\[\tag{2} \int_{-\infty}^{+\infty}x^m \psi(x) dx = 0 \;, \;\; \;0 \leq m < n_\psi\; . \]

As originally pointed out by Mallat and collaborators (Mallat & Hwang, 1992; Mallat & Zhong,
1992), for the specific purpose of analyzing the regularity of a function, one can get rid of the
redundancy of the WT by concentrating on the WT skeleton defined by its modulus maxima only.
These maxima are defined, at each scale \(a\ ,\) as the local maxima of \(|T_\psi[f](x,a)|\) considered as a function of \(x\ .\)
As illustrated in Figure 2(e,f),
these WTMM are disposed on connected curves in the space-scale (or time-scale) half-plane,
called *maxima lines*.
Let us define \(\mathcal{L}(a_0)\) as the set
of all the maxima lines that exist at the scale \(a_0\)
and which contain maxima at any scale \(a \leq a_0\ .\)
An important feature of these maxima lines, when analyzing singular functions, is that there is at least
one maxima line pointing towards each singularity (Mallat & Hwang, 1992; Mallat & Zhong,
1992; Muzy et al., 1994).

### Analyzing wavelets

There are almost as many analyzing wavelets as applications of the continuous WT (Arneodo et al., 1988, 1989, 1992, 1995b; Bacry et al., 1993; Muzy et al., 1991, 1993, 1994). A commonly used class of analyzing wavelets is defined by the successive derivatives of the Gaussian function:

\[\tag{3} g^{(N)}(x)=\frac{d^N}{dx^N}e^{-x^2/2} \; , \]

for which \(n_\psi=N\) and more specifically \(g^{(1)}\) and \(g^{(2)}\) that are illustrated in Figure 1(a,b). Note that the WT of a signal \(f\) with \(g^{(N)}\) (Eq. (3)) takes the following simple expression:

\[\tag{4} \begin{array}{lcl} T_{g^{(N)}}[f](x,a) &=& \frac{1}{a} \int_{-\infty}^{+\infty} f(y) g^{(N)} \left( \frac{y-x}{a} \right) dy, \\ &=& a^N \frac{d^N}{dx^N}T_{g^{(0)}}[f](x,a). \end{array} \]

Equation (4) shows that the WT computed with \(g^{(N)}\) at scale \(a\) is nothing but the Nth derivative of the signal \(f(x)\) smoothed by a dilated version \(g^{(0)}(x/a)\) of the Gaussian function. This property is at the heart of various applications of the WT microscope as a very efficient multi-scale singularity tracking technique (Arneodo et al., 2002).

### Scanning singularities with the wavelet transform modulus maxima

The strength of the singularity of a function \(f\) at point \(x_0\) is given
by the *Hölder* exponent, *i.e.*, the largest exponent such that there
exists a polynomial \(P_n(x-x_0)\) of order \(n < h(x_0)\) and
a constant \(C > 0\ ,\) so that for any point \(x\) in a neighborhood of \(x_0\ ,\) one has
(Bacry et al., 1993; Holschneider & Tchamitchian, 1990; Jaffard, 1989, 1991; Mallat & Hwang, 1992; Mallat & Zhong,
1992; Muzy et al., 1994)\[\tag{5}
|f(x) - P_n(x-x_0)| \leq C|x-x_0|^h.
\]

If \(f\) is \(n\) times continuously differentiable at the point \(x_0\ ,\) then one can use for the polynomial \(P_n(x-x_0)\ ,\) the order-\(n\) Taylor series of \(f\) at \(x_0\) and thus prove that \(h(x_0) > n\ .\) Thus \(h(x_0)\) measures how irregular the function \(f\) is at the point \(x_0\ .\) The higher the exponent \(h(x_0)\ ,\) the more regular the function \(f\ .\)

The main interest in using the WT for analyzing the regularity of a function lies in its ability to be blind to polynomial behavior by an appropriate choice of the analyzing wavelet \(\psi\ .\) Indeed, let us assume that according to Eq.(5), \(f\) has, at the point \(x_0\ ,\) a local scaling (Hölder) exponent \(h(x_0)\ ;\) then, assuming that the singularity is not oscillating (Arneodo et al., 1997a, 1998b; Mallat & Zhong, 1992), one can easily prove that the local behavior of \(f\) is mirrored by the WT which locally behaves like (Arneodo et al., 1995b; Bacry et al., 1993; Holschneider & Tchamitchian, 1990; Jaffard, 1989, 1991, 1997a,b; Mallat & Hwang, 1992; Mallat & Zhong, 1992; Muzy et al., 1991, 1993, 1994)\[\tag{6} T_\psi[f](x_0,a) \sim a^{h(x_0)}\; , a \rightarrow 0^+\; , \]

provided \(n_\psi>h(x_0)\ ,\) where \(n_\psi\) is the number of vanishing moments of \(\psi\) (Eq.(2)). Therefore one can extract the exponent \(h(x_0)\) as the slope of a log-log plot of the WT amplitude versus the scale \(a\ .\) On the contrary, if one chooses \(n_\psi < h(x_0)\ ,\) the WT still behaves as a power-law but with a scaling exponent which is \(n_\psi\ :\) \(\tag{7} T_\psi[f](x_0,a) \sim a^{n_\psi}\; , a \rightarrow 0^+\; . \)

Thus, around a given point \(x_0\ ,\) the faster the WT
decreases when the scale goes to zero, the more regular \(f\) is
around that point. In particular,
if \(f\in C^\infty\) at \(x_0\) (\(h(x_0) = +\infty\)), then the WT scaling
exponent is given by \(n_\psi\ ,\)
*i.e.* a value which is dependent on the shape of the analyzing wavelet.
According to this observation, one can hope to detect the points where
\(f\) is smooth by just checking
the scaling behavior of the WT when increasing the order \(n_\psi\)
of the analyzing wavelet (Arneodo et al., 1995b; Bacry et al., 1993; Muzy et al., 1991, 1993, 1994).

**Remark**

A very important point (at least for practical purpose) raised by Mallat and Hwang (Mallat & Hwang, 1992) is that the local scaling exponent \(h(x_0)\) can be equally estimated by looking at the value of the WT modulus along a maxima line converging towards the point \(x_0\ .\) Indeed one can prove that both Eqs.(6) and (7) still hold when following a maxima line from large down to small scales (Mallat & Hwang, 1992; Mallat & Zhong, 1992).

## The wavelet transform modulus maxima method for multifractal analysis

### Singularity spectrum

As originally defined by Parisi & Frisch (1985), the multifractal formalism of multi-affine functions
amounts to compute the so-called *singularity spectrum* \(D(h)\) defined as the Hausdorff dimension of the set where the Hölder exponent is equal to
\(h\) (Arneodo et al., 1995b; Bacry et al., 1993; Muzy
et al., 1994):

\[\tag{8} D(h)=dim_H\{ x\;,\; h(x)=h \} \; , \]

where \(h\) can take, *a priori*, positive as well as
negative real values (*e.g.*, the Dirac distribution
\(\delta(x)\) corresponds to the Hölder exponent \(h(0)=-1\)) (Jaffard, 1997a).

### The WTMM method

A natural way of performing a multifractal analysis of fractal functions consists in generalizing the “classical” multifractal formalism (Collet et al., 1987; Grassberger et al., 1988; Halsey et al., 1986; Paladin & Vulpiani, 1987; Rand, 1989) using wavelets instead of boxes. By taking advantage of the freedom in the choice of the “generalized oscillating boxes” that are the wavelets, one can hope to get rid of possible smooth behavior that could mask singularities or perturb the estimation of their strength \(h\ .\) But the major difficulty with respect to box-counting techniques (Argoul et al., 1990; Farmer et al., 1983; Grassberger & Procaccia, 1983; Grassberger et al., 1988; Meneveau & Sreenivasan, 1991) for singular measures, consists in defining a covering of the support of the singular part of the function with our set of wavelets of different sizes. As emphasized in (Arneodo et al., 1995b; Bacry et al., 1993; Muzy et al., 1991, 1993, 1994), the branching structure of the WT skeletons of fractal functions in the \((x,a)\) half-plane enlightens the hierarchical organization of their singularities ( Figure 2(e,f)). The WT skeleton can thus be used as a guide to position, at a considered scale \(a\ ,\) the oscillating boxes in order to obtain a partition of the singularities of \(f\ .\) The wavelet transform modulus maxima (WTMM) method amounts to compute the following partition function in terms of WTMM coefficients (Arneodo et al., 1995b; Bacry et al., 1993; Muzy et al., 1991, 1993, 1994):

\[\tag{9} Z(q,a)=\sum_{l\in \mathcal{L}(a)} {\left( \sup_{\stackrel{\scriptstyle{(x,a')\in l}}{\scriptstyle{a'\leq a}}} \vert T_\psi[f](x,a')\vert \right)}^q \; , \]

where \(q\in\mathbb{R}\) and the \(\sup\) can be regarded as a way to define a scale adaptive “Hausdorff-like” partition. Now from the deep analogy that links the multifractal formalism to thermodynamics (Arneodo et al., 1995b; Bohr & Tel, 1988), one can define the exponent \(\tau(q)\) from the power-law behavior of the partition function:

\[\tag{10} Z(q,a) \; \sim \; a^{\tau(q)} \; ,\;\; a \rightarrow 0^+\; , \]

where \(q\) and \(\tau(q)\) play respectively the role of the inverse
temperature and the free energy.
The main result of this wavelet-based multifractal
formalism is that in place of the energy and the entropy
(*i.e.* the variables conjugated to \(q\) and \(\tau\)),
one has \(h\ ,\) the Hölder exponent, and \(D(h)\ ,\) the
singularity spectrum. This means that the
singularity spectrum of \(f\) can be determined from the Legendre transform
of the partition function scaling exponent \(\tau(q)\)(Bacry et al., 1993; Jaffard,
1997a,b):

\[\tag{11} D(h)=\min_q(qh-\tau(q)) \; . \]

### Monofractal versus multifractal functions

From the properties of the Legendre transform, it is easy to see that
*homogeneous* monofractal functions that involve singularities of unique Hölder exponent
\(h=\partial \tau/\partial q\ ,\) are characterized by a \(\tau(q)\) spectrum which is a *linear*
function of \(q\) (Figure 3(c)).
On the contrary, a *nonlinear* \(\tau(q)\) curve is the signature of nonhomogeneous functions that
exhibit *multifractal* properties, in the sense that the Hölder exponent \(h(x)\) is a
fluctuating quantity that depends upon the spatial position \(x\) (Figure 3(c)).
As illustrated in Figure 3(d), the \(D(h)\) singularity spectrum of a multifractal
function displays a single humped shape that characterizes intermittent fluctuations corresponding
to Hölder exponent values spanning a whole interval \([h_{min}, h_{max}]\ ,\) where \(h_{min}\) and
\(h_{max}\) are the Hölder exponents of the strongest and weakest singularities respectively.

## Applications of the WTMM method

### DNA sequences: monofractality of DNA walks

A DNA sequence is a four-letter (A, C, G, T) text where A, C, G and T stand for the bases adenine, cytosine, guanine and thymine respectively. A popular method to graphically portray the genetic information stored in DNA sequences is to used the so-called “DNA walk” representation (Peng et al., 1992). It consists first in converting the DNA text into a binary sequence by coding for example with \(\chi(i)=1\) at a given nucleotide positions and \(\chi(i)=-1/3\) at other positions (Voss, 1992), and then in defining the graph of the DNA walk by the cumulative variables \(f(n)=\sum_{i=1}^n\chi(i)\ .\) The DNA walk obtained with the “G” mononucleotide coding for the largest intron of the human dystrophin gene is shown in Figure 2(a) for illustration. Figure 2(c) illustrates the WT when using an analyzing wavelet of sufficiently high order, namely \(g^{(2)}\) (\(n_\psi=2\)), to get rid of the linear trends in the DNA walk landscape inherent to the heterogeneity of composition of genomic sequences (Arneodo et al., 1995a, 1996). Figure 3(a) displays some plots of \(\log_2Z(q,a)\) vs \(\log_2(a)\) for different values of \(q\ ,\) where the partition function \(Z(q,a)\) has been computed on the WTMM skeleton (Figure 2(e)), according to the definition (Eq. (9)) for a set of 2184 human introns of size \(L\geq800\)bp. Using a linear regression fit, we then obtain the slopes \(\tau(q)\) of these graphs. As shown in Figure 3(c), when plotted versus \(q\ ,\) the data for the exponents \(\tau(q)\) consistently fall on a straight line that is remarkably fitted by

\[\tag{12} \tau(q) = qH-1 \; , \]

with \(H=0.60\pm0.02\ .\) From the Legendre transform of this linear \(\tau(q)\) (Eq. (11)), one gets a \(D(h)\) singularity spectrum that reduces to a single point:

\[\tag{13} \begin{array}{lcl} D(h) = 1 \; & \text{if} & \; h = H \; ,\\ = -\infty \; & \text{if} & \; h \neq H \; , \end{array} \]

as the signature of a nowhere differentiable homogeneous fractal signal
with a unique Hölder exponent \(h=H=0.60\ .\)
Note that similar good estimates are obtained when using analyzing wavelets of different
order (*e.g.* \(g^{(3)}\)).

Within the perspective of confirming the monofractality of DNA walks, we have studied the probability density function (pdf) of wavelet coefficient values \(\rho_a(T_{g^{(2)}}(.,a))\ ,\) as computed at a fixed scale \(a\) in the fractal scaling range. According to the monofractal scaling properties, one expects these pdfs to satisfy the self-similarity relationship (Arneodo et al., 1995a, 1996, 2002):

\[\tag{14} a^H \rho_a(a^H T) = \rho(T) \; , \]

where \(\rho(T)\) is a “universal” pdf (actually the pdf obtained at scale \(a=1\))
that does not depend on the scale parameter \(a\ .\)
As shown in Figure 4(a,c), when plotting
\(a^H \rho_a(a^H T)\) vs \(T\ ,\) all the \(\rho_a\) curves corresponding to
different scales (Figure 4(a)) remarkably collapse on
a unique curve when using a unique exponent \(H=0.60\) (Figure 4(c)).
Furthermore the so-obtained universal curve cannot be distinguished from
a parabola in semi-log representation as the signature of monofractal
Gaussian statistics.
Therefore, the fluctuations of DNA walks about the composition induced linear
trends cannot be distinguished from persistent fractional Brownian motion
(fBm) \(B_{H=0.60}\) that display long-range correlations (LRC)
(\(H>0.5\)) (Arneodo et al., 1996, 2002; Muzy et al., 1994).
Similar LRC were found in non-coding sequences as well as in coding regions
(*e.g.* coding exons) in eukaryotic genomes (but not for eubacterial sequences
for which \(H=0.5\)) as the signature of nucleosomal structure, the first step of
compaction of DNA in eukaryotic nuclei (Audit et al., 2001, 2002).

### Fully developed turbulence

It is now well accepted (Frisch, 1995) that in the fully developed regime, a turbulent flow is likely to be in a universal state that can be experimentally characterized by statistical quantities such as the multifractal spectra \(\tau(q)\) and \(D(h)\ .\) For more than thirty years, one of the main features recognized experimentally is the intermittency of small scales (Frisch, 1995; Meneveau & Sreenivasan, 1991; Monin & Yaglom, 1975) which manifests in a significant departure of the experimental velocity data from the monofractal prediction \(\tau(q)=q/3-1\) of Kolmogorov (K41) (Kolmogorov, 1941) based on the homogeneity assumption that, at each point of the fluid, the longitudinal velocity increments have the same scaling behavior \(\delta v_l(x)\sim l^{1/3}\ ,\) which yields the well known \(E(k)\sim k^{-5/3}\) energy spectrum (Frisch, 1995). The pioneering studies (Anselmet et al., 1984; Frisch, 1995) were performed using the structure functions method which, as discussed in Muzy et al. (1993), intrinsically fails to fully characterize the \(D(h)\) singularity spectrum.

In Figure 2(b,d,f), Figure 3(b,c,d) and Figure 4(b,d) are reported the results of a multifractal analysis of single point longitudinal velocity data from high Reynolds 3D turbulence using the WTMM method (Arneodo et al., 1998a,c, 1999b; Delour et al., 2001). The data were obtained by Gagne and collaborators in the large wind tunnel S1 of ONERA at Modane. The Taylor scale based Reynolds number is \(R_\lambda\simeq 2000\) and the extent of the inertial range following approximately the Kolmogorov \(k^{-5/3}\) law is about four decades (integral scale \(L\simeq7\)m, dissipation scale \(\eta\simeq0.27\)mm). In Figure 2(b) is illustrated a sample of the longitudinal velocity signal of length of about two integral scales, when using the Taylor hypothesis (Frisch, 1995). The corresponding WT and WT skeleton as computed with \(g^{(2)}\) are shown inFigure 2(d) and Figure 2(f) respectively. As shown in Figure 3(b), when plotted versus the scale parameter \(a\) in a logarithmic representation, the annealed average over 28000 integral scales of the partition functions \(Z(q,a)\) displays a well defined scaling behavior in the inertial range for a rather wide range of \(q\) values\[-4\leq q\leq 7\ .\] When processing to a linear regression fit of the data, one gets a non-linear \(\tau(q)\) spectrum, the hallmark of multifractal scaling, that is well approximated by the quadratic spectrum of log-normal processes:

\[\tag{15} \tau(q) =c_1q-\frac{c_2}{2}q^2-d\; , \]

with \(c_1=0.36\pm0.02\ ,\) \(c_2=0.028\pm0.003\) and where \(d=1\) is the spatial dimension (1D cut of the 3D velocity field). Similar, quantitative agreement is observed for the \(D(h)\) singularity spectrum in Figure 3(d) which displays a remarkable parabolic shape:

\[\tag{16} D(h) = d - \frac{(h+c_1)^2}{2c_2}\; , \]

that characterizes intermittent fluctuations corresponding to Hölder exponent
values ranging from \(h_{min}=0.12\) to \(h_{max}=0.60\ ,\) the largest dimension
\(D(h(q=0))=-\tau(0)=0.999\pm0.001=d\) being attained for \(h=c_1=0.36\pm0.02\ ,\)
*i.e.*, a value which is slightly larger than the K41 prediction \(h=1/3\ .\)
This multifractal diagnosis is confirmed in Figure 4(b) where the pdf
of WT coefficients has a shape which evolves across scales from Gaussian at large
scales to more intermittent profiles with stretched exponential-like tails at smaller
scales.
As illustrated in Figure 4(d), there is no way to collapse all the WT pdfs
on a single curve with a unique exponent \(H\) as expected from the self-similarity
relationship (14).
Instead, this can be done (Arneodo et al., 1997b, 1998c, 1999b) by using
a Gaussian kernel that strongly supports the log-normal cascade
phenomenology (Castaing
et al., 1990; Delour et al., 2001; Kolmogorov, 1962; Oboukhov, 1962) of
fully developed turbulence.

## Generalizing the WTMM method to d-dimensional image analysis

The generalization of the WTMM method in higher dimension is directly
inspired from Mallat *et al.* (Mallat & Hwang, 1992; Mallat & Zhong, 1992)
reformulation of Canny multiscale edge detector (Canny, 1986) in terms of 2D
WT. The general idea is to start smoothing the discrete image data by
convolving it with a filter and then compute the gradient of the
smoothed image. This method has been implemented, tested and applied
to 2D (Arneodo et al., 1999a, 2000, 2003; Decoster et al., 2000; Roux et al., 2000) and
3D (Kestener & Arneodo, 2003) scalar field.

### The d-dimensional WTMM method

Let us define d analyzing wavelet \(\psi_i(\mathbf{x}=(x_1,x_2,\ldots,x_d))\) that are respectively, the partial derivatives of a smoothing scalar function \(\phi(\mathbf{x})\ :\) \[\tag{17} \psi_i(\mathbf{x}=(x_1,x_2,\ldots,x_d))=\partial \phi(\mathbf{x}=(x_1,x_2,\ldots,x_d))/\partial x_i\; , \;\; i=1,2,\ldots,d. \]

\(\phi(\mathbf{x})\) is supposed to be an isotropic function that depends on
\(|\mathbf{x}|\) only and that is well localized around \(|\mathbf{x}|=0\ .\) Commonly used
smoothing functions are the Gaussian function :

\[\tag{18} \phi(\mathbf{x}=(x_1,x_2,\ldots,x_d))=e^{-|\mathbf{x}|^2/2}, \]

and the isotropic Mexican hat :

\[\tag{19} \phi(\mathbf{x}=(x_1,x_2,\ldots,x_d))=(d-x^2) e^{-|\mathbf{x}|^2/2}, \]

that correspond to a first-order (\(n_{\boldsymbol{\psi}}=1\)) and a third-order (\(n_{\boldsymbol{\psi}}=3\)) analyzing wavelet respectively.

For any scalar function \(f(x_1,x_2,\ldots,x_d)\in L^2\)(\(\mathbb{R}^d\)), the WT at point \(\mathbf{b}\) and scale \(a\) can be expressed in a vectorial form (Arneodo et al., 2000; Decoster et al., 2000; Kestener & Arneodo, 2003):

\[\tag{20} {\mathbf T}_{\boldsymbol{\psi}} [f] ({\mathbf b},a)= \left\{\begin{array}{l} T_{\psi_1}[f] = a^{-d} \int d^d {\mathbf x} \; \psi_1 \bigl( a^{-1} ( {\mathbf x} - {\mathbf b} ) \bigr) f( {\mathbf x} ) \\ T_{\psi_2}[f] = a^{-d} \int d^d {\mathbf x} \; \psi_2 \bigl( a^{-1} ( {\mathbf x} - {\mathbf b} ) \bigr) f( {\mathbf x} )\\ \qquad\quad\vdots \\ T_{\psi_d}[f] = a^{-d} \int d^d {\mathbf x} \; \psi_d \bigl( a^{-1} ( {\mathbf x} - {\mathbf b} ) \bigr) f( {\mathbf x} ) \end{array}\right\} \]

Then, after a straightforward integration by parts, \({\mathbf
T}_{\boldsymbol{\psi}}\) can be expressed as the gradient vector field of \(f(\mathbf{x})\)
smoothed by dilated versions \(\phi(\mathbf{x}/a)\) of the smoothing filter. At
a given scale \(a\) the WTMM are defined by the positions \(\mathbf{b}\) where
the modulus \(\mathcal{M}_{\boldsymbol{\psi}}[f]({\mathbf b},a)=|{\mathbf T}_{\boldsymbol{\psi}}[f]
(\mathbf{b},a)|\) is locally maximum along the direction of the WT
vector. These WTMM lie on connected (d-1) hypersurfaces called maxima
hypersurfaces (see Figure 5 and Figure 8).
In theory, at each scale \(a\ ,\) one only needs to record the position of
the local maxima of \(\mathcal{M}_{\boldsymbol{\psi}}\) along the maxima
hypersurfaces together with the value of \(\mathcal{M}_{\boldsymbol{\psi}}[f]\) and the
direction \(\mathcal{A}_{\boldsymbol{\psi}}[f]({\mathbf b},a)\) of \({\mathbf T}_{\boldsymbol{\psi}}[f]\ .\)
These WTMMM are disposed along connected curves across scales
called maxima lines living in a (d+1)-space
\((x_1,x_2,\ldots,x_d,a)\ .\) The WT skeleton is then defined as the set
of maxima lines that converge to the \((x_1,x_2,\ldots,x_d)\) hyperplane
in the limit \(a\rightarrow 0^+\) (see Figure 6). As originally
demonstrated in Arneodo et al. (1999a),
Decoster et al. (2000) and Kestener & Arneodo (2003),
the local Hölder
regularity of \(f(\mathbf{x})\) can be estimated from the power-law behavior of
\(\mathcal{M}_{\boldsymbol{\psi}}[f] \bigl(\mathcal{L}_{{\mathbf x}_0}(a) \bigr)\sim a^{h({\mathbf x}_0)}\)
along the maxima line \(\mathcal{L}_{{\mathbf x}_0}(a)\) pointing to the
point \({\mathbf x}_0\) in the limit \(a\rightarrow 0^+\ ,\) provided
\(h({\mathbf x}_0)\) be smaller than the number \(n_{\boldsymbol{\psi}}\)
(\(=\min_j n_{\psi_j}\)) of zero moments of the analyzing wavelet \(\boldsymbol{\psi}\ .\) Then,
very much like in 1D (Eq. (9)), one can use the
scale-partitioning given by the WT skeleton to define the following
partition functions :

\[\tag{21} {\mathcal Z}(q,a)=\sum_{{\mathcal L}\in {\mathcal L}(a)} \left ( \mathcal{M}_{\boldsymbol{\psi}}[f]({\mathbf x},a)\right)^q \; , \]

where \(q\in\mathbb{R}\) and \({\mathcal L}(a)\) is the set of maxima lines that exist
at scale \(a\) in the WT skeleton. As before, the \(\tau(q)\) spectrum
will be extracted from the scaling behavior of \({\mathcal Z}(q,a)\)
(Eq. (10)) and in turn the \(D(h)\) singularity spectrum will be
obtained from the Legendre transform of \(\tau(q)\)
(Eq. (11)) (Arneodo et al., 1999a; Decoster et al., 2000; Kestener & Arneodo, 2003).

### Application of the 2D WTMM method to high-resolution satellite images of cloud structure

Stratocumulus are one of the most studied clouds types (Davis et al., 1996). Being at once persistent and horizontal extended, marine Sc layers are responsible for a large portion of the earth's global albedo, hence, its overall energy balance.Figure 5(a) shows a typical 1024x1024 pixels portion among 14 overlapping subscenes of the original Sc Landsat images where quasi-nadir viewing radiance at satellite level is digitized on an eight-bit grey scale. The different steps of the 2D WTMM methodology are illustrated in Figure 5 (b,c,d) where the WTMM chains and the local maxima of \(\mathcal{M}_{\boldsymbol{\psi}}\) along these chains computed with the first order (\(n_{\boldsymbol{\psi}}=1\)) analyzing wavelet, are shown at different scales. In Figure 7 are reported the \(\tau(q)\) and \(D(h)\) multifractal spectra obtained from the scaling behavior of \({\mathcal Z}(q,a)\) over the range of scales \(390\) m \(\lesssim a \lesssim 3120\) m (Arneodo et al., 1999a; Roux et al., 2000). Both spectra are clearly non linear and very well fitted by the theoretical quadratic spectra of log-normal cascade processes (Eqs (15) and (16) with \(d=2\)). However, with the first-order analyzing wavelet, the best fit is obtained with the parameter values \(c_1=0.38\pm 0.02\) and \(c_2=0.070\pm 0.005\ ,\) while for the third-order wavelet these parameters take slightly different values, namely \(c_1=0.37\pm 0.02\) and \(c_2=0.060\pm 0.005\ .\) The intermittency coefficient \(c_2\) is therefore somehow reduced when going from \(n_{\boldsymbol{\psi}}=1\) to \(n_{\boldsymbol{\psi}}=3\ .\) Actually, it is a lack of statistical convergence because of insufficient sampling which is the main reason for this uncertainty in the estimate of \(c_2\ .\)

In Figure 7(b) are shown for comparison the \(D(h)\) singularity
spectra of turbulent longitudinal velocity data recorded at the Modane
wind tunnel (\(R_\lambda\simeq 2000\)) and of temperature fluctuations
recorded in a \(R_\lambda=400\) turbulent flow (Ruiz-Chavarria et al., 1996).
The \(D(h)\)
curve for marine Sc clouds is much wider than the velocity \(D(h)\)
spectrum (the intermittency coefficient \(c_2\) being almost three time
larger) and it is rather close to the temperature \(D(h)\) spectrum. If
it is well recognized that liquid water is not really passive, the
results derived with the 2D WTMM method in Figure 7 show that
from a multifractal point of view, the intermittency captured by the
Landsat satellite looks statistically equivalent to the intermittency
of a passive scalar in fully developed 3D turbulence. The fact that
the internal structure of Sc cloud somehow reflects some statistical
properties of atmospheric turbulence is not such a surprise in this
highly turbulent environment (Arneodo et al., 1999a; Roux et al., 2000).

### Application of the 3D WTMM method to 3D isotropic turbulence simulations

A central quantity in the K41 theory of fully developed turbulence is the mean energy dissipation \(\epsilon = \frac{\nu}{2} \sum_{i,j}(\partial_j v_i +\partial_i v_j)^2\) which is supposed to be constant. Indeed, \(\epsilon\) is not spatially homogeneous but undergoes local intermittent fluctuations (Frisch, 1995; Meneveau & Sreenivasan, 1991). There have been early numerical and experimental attempts to measure the multifractal spectra of \(\epsilon\) or of its 1D surrogate approximation \(\epsilon'=15\nu (\partial u/\partial x)^2\) (where \(u\) is the longitudinal velocity) using classical box counting techniques (Meneveau & Sreenivasan, 1991). In Figure 8 and Figure 9 are reported the results of the application of the 3D WTMM method (Kestener & Arneodo, 2003) to isotropic turbulence direct numerical simulations (DNS) data obtained by Meneguzzi with the same numerical code as previously used by Vincent & Meneguzzi (1991), but at a \((512)^3\) resolution and a viscosity of \(5.10^{-4}\) corresponding to a Taylor Reynolds number \(R_\lambda = 216\) (one snapshot of the dissipation 3D spatial field). The main steps of the 3D WT computation are illustrated in Figure 8. Note that the WTMMM points that define the WT skeleton, now lie on WTMM 2D surfaces at a given scale. The multifractal spectra obtained from this WT skeleton are shown in Figure 9. The \(\tau_\epsilon(q)\) spectrum in Figure 9(a) significantly deviates from a straight line the hallmark of multifractality. But surprisingly, the data obtained from the 3D WTMM method \(\tau_\epsilon^{\mathrm{WT}}(q)\) significantly differ from the spectrum \(\tau_\epsilon(q)=\tau_\epsilon^{\mathrm{BC}}(q)-3q\) estimated with box-counting technique (Kestener & Arneodo, 2003). Actually the WT estimate of the cancellation exponent \(\tau_\epsilon^{\mathrm{WT}}(q=1)+3=-0.19\pm 0.03 < 0\ ,\) the signature of a signed measure. Indeed, as shown in Figure 9(a), the \(\tau_\epsilon^{\mathrm{WT}}(q)\) data are rather nicely fitted by the theoretical spectrum \(\tau_\mu(q)\) of the nonconservative binomial \(p\) model (Mandelbrot, 1974; Meneveau & Sreenivasan, 1991) with weights \(p_1=0.36\) and \(p_2=0.78\) (\(p_1+p_2=1.14>1\)). By construction, the BC algorithms systematically provide a misleading conservative \(\tau_\epsilon(q)\) spectrum diagnostic with \(p=p_1/(p_1+p_2)=0.32\) and \(1-p=p_2/(p_1+p_2)=0.68\ .\) The difference between the two spectra is nothing but a fractional integration of exponent \(H^*=\log_2(p_1+p_2)\sim 0.19\ .\) This result is confirmed in Figure 9(b) where the singularity spectrum \(D_\epsilon^{\mathrm{BC}}(h)\) is misleadingly shifted to the right by \(H^*\) (\(= -\) the cancellation exponent), without any change of shape as compared to \(D_\epsilon^{\mathrm{WT}}(h)\) (Kestener & Arneodo, 2003). This observation seriously questions the validity of most of the experimental and numerical BC estimates of \(\tau_\epsilon^{\mathrm{BC}}(q)\) and \(f_\epsilon^{\mathrm{BC}}(\alpha)=D_\epsilon^{\mathrm{BC}}(h+3)\) spectra reported so far in the literature. Besides the fact that the \(\tau_\epsilon^{\mathrm{WT}}(q)\) and \(D_\epsilon^{\mathrm{WT}}(h)\) spectra seem to be even better fitted by a parabola, as predicted for non-conservative log-normal cascade processes, these results raise the fundamental question of the possible asymptotic decrease to zero of the cancellation exponent in the infinite Reynolds number limit.

## Perspectives

For many years, the multifractal description has been mainly devoted to scalar measures and functions. However, in physics as well as in other fundamental and applied sciences, fractals appear not only as deterministic or random scalar fields but also as vector-valued deterministic or random fields. Very recently, Kestener & Arneodo (2004, 2007) have combined singular value decomposition techniques and WT analysis to generalize the multifractal formalism to vector-valued random fields. The so-called Tensorial Wavelet Transform Modulus Maxima (TWTMM) method has been applied to turbulent velocity and vorticity fields generated in \((256)^3\) DNS of the incompressible Navier-Stokes equations. This study reveals the existence of an intimate relationship \(D_V(h+1)=D_\omega(h)\) between the singularity spectra of these two vector fields that are found significantly more intermittent that previously estimated from longitudinal and transverse velocity increment statistics. Furthermore, thanks to the singular value decomposition, the TWTMM method looks very promising for future simultaneous multifractal and structural (vorticity sheets, vorticity filaments) analysis of turbulent flows (Kestener & Arneodo, 2004, 2007).

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## Recommended reading

- P. Abry, P. Gonçalvès & J. Lévy-Véhel eds. In French: (2002).
*Lois d’échelle, fractales et ondelettes*. Hermès, Paris. In English: (2005).*Scaling Fractals And Wavelets*. iSTE Publishing Company. - A. Arneodo, F. Argoul, E. Bacry, J. Elezgaray & J.-F. Muzy (1995).
*Ondelettes, Multifractales et Turbulences : de l'ADN aux Croissaces Cristallines.*Diderot Editeur, Art et Sciences, Paris. - A. Bunde, J. Kropp & H. J. Schellnhuber, eds. (2002).
*The Science of Disasters: Climate Disruptions, Heart Attacks, and Market Crashes*. Springer Verlag, Berlin. - U. Frisch (1995).
*Turbulence*. Cambridge University Press, Cambridge. - S. Jaffard, Y. Meyer & R. D. Ryan eds. (2001).
*Wavelets: Tools for Science & Technology.*SIAM, Philadelphia. - S. Mallat (1998).
*A Wavelet Tour in Signal Processing*. Academic Press, New-York. - B. Torresani (1998).
*Analyse Continue par Ondelettes.*Editions de Physique, Les Ulis.

## External links

- Pierre Kestener's website.
- Stéphane Roux's website.
- Benjamin Audit's website.
- Patrice Abry's website.
- Emmanuel Bacry's website.
- Stéphane Jaffard's website.
- SISYPHE and Chromatin and Genome groups of Laboratoire de Physique in Ecole Normale Supérieure de Lyon.
- LastWave signal processing software includes an implementation of 1D WTMM method.
- WaveLab: Free MatLab function collection for wavelet analysis.
- Wavelet.org: maintains an important list of useful wavelet-related links.