Reproducing Polynomials with B-Splines

Version vom 19. Juli 2010, 16:45 Uhr von Niki (Diskussion | Beiträge)

(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu: Navigation, Suche
Family of B-splines up to N=4

A B-Spline of order N is known to be able to reproduce any polynomial up to order N:

\sum_{n \in \mathbb{Z}} c_{m,n} \beta_N (t - n) = t^m

In words, a proper linear combination of shifted versions of a B-Spline can reproduce any polynomial up to order N. This is needed for different applications, for example, for the Sampling at Finite Rate of Innovation (FRI) framework. In this case any kernel \varphi reproducing polynomials (that is, satisfying the Strang-Fix conditions) can be used. However, among all possible kernels, the B-Splines have the smallest possible support.

An important question is how to obtain the coefficients c_{m,n} for the reproduction-formula. In this small article, I describe one way.

Starting from

\sum_{n \in \mathbb{Z}} c_{m,n} \varphi(t - n) = t^m

the coefficients can be obtained using the dual of \varphi, \tilde{\varphi}[1] (I set \beta_N = \varphi for consistency with my notes):

c_{m,n} = \int_{-\infty}^{\infty} t^m \tilde{\varphi}(t - n)\,dt

However, even if the dual would be known, solving the infinite integral is only feasible when the dual has finite support. This is the case with the B-Spline itself but not with its dual!

A closer look at the formula tells that this is nothing more than a convolution (under the assumption that \tilde{\varphi} is symmetric which is the case):

c_{m,n} = \int t^m \tilde{\varphi}(-(n-t))\,dt = \int t^m \tilde{\varphi}(n-t)\,dt = (t^m * \tilde{\varphi})(n)

Now, this can be transformed to fourier domain:

(t^m * \tilde{\varphi})(n) = \mathcal{F}^{-1}\left\{ \mathcal{F}\left\{t^m\right\} \tilde{\Phi}(\omega)\right\} =  \mathcal{F}^{-1}\left\{ j^m \sqrt{2\pi} \delta^{(n)}(\omega) \tilde{\Phi}(\omega) \right\} = j^m \sqrt{2\pi} \mathcal{F}^{-1}\left\{\delta^{(n)}(\omega) \tilde{\Phi}(\omega) \right\}

Writing the inverse of this expression yields:

j^m \sqrt{2\pi} \frac{1}{\sqrt{2\pi}} \int_{-\pi}^{\pi} \delta^{(n)}(\omega) \tilde{\Phi}(\omega) e^{j\omega n}\,d\omega = j^m \int_{-\infty}^{\infty} \delta^{(n)}(\omega) \underbrace{\tilde{\Phi}(\omega) e^{j\omega n}}_{f(\omega)}\,d\omega

It is known that[2]:

\int \delta^{(n)}(x) f(x)\,dx = (-1)^n f^{(n)}(0)

so that

j^m \int_{-\infty}^{\infty} \delta^{(n)}(\omega) f(\omega)\,d\omega = j^m (-1)^m \left. \frac{\partial^m}{\partial \omega^m} f(\omega) \right|_{\omega = 0}

Now the whole procedure has been reduced to calculating the derivative of f(\omega) and set the result to zero.

An open question is how to obtain the dual of \varphi. As the reproduction formula spans a vector space, \varphi must be at least bi-orthogonal to \tilde{\varphi}. This translates in fourier domain to[3]:

\tilde{\Phi}(\omega) = \frac{\Phi(\omega)}{\sum_{k \in \mathbb{Z}} |\Phi(\omega + 2\pi k)|^2}

The fourier transform of a B-Spline of order N is (e.g. [4]):

\Beta_N(\omega) = \Phi(\omega) = \left( \frac{\sin(\omega/2)}{\omega/2} \right)^{N+1} =

The following derivation of the sum is borrowed from [5]. For this derivation to work, I set L=N+1 temporarily:

\sum_{k \in \mathbb{Z}} |\Phi(\omega + 2\pi k)|^2 = 
\sum_{k \in \mathbb{Z}} \left|\mathrm{sinc}\left(\frac{1}{2}(\omega + 2\pi k)\right)^L \right|^2 =
\sum_{k \in \mathbb{Z}} \left|\mathrm{sinc}\left(\frac{1}{2}(\omega + 2\pi k)\right) \right|^{2L}

and because 2L is always even:

= \sum_{k \in \mathbb{Z}}\frac{\sin^{2L}(\frac{1}{2}(\omega + 2\pi k))}{\left(\frac{1}{2}(\omega + 2\pi k)\right)^{2L}}
= \sum_{k \in \mathbb{Z}}\frac{\sin^{2L}(\frac{\omega}{2} + \pi k))}{(\frac{\omega}{2} + \pi k)^{2L}}

Because of the periodicity it is known that

\sin^{2L}(x + \pi k) = \sin^{2L}(x)

such that

= \sin^{2L}\left(\frac{\omega}{2}\right) \sum_{k \in \mathbb{Z}}\frac{1}{(\frac{\omega}{2} + \pi k)^{2L}}

And finally the following relation is used[6]:

\sum_k \frac{1}{(x + \pi k)^{2L}} = -\frac{1}{(2L-1)!} \frac{d^{2L-1}}{dx^{2L-1}} \cot{x}

in order to finally obtain:

\sum_{k \in \mathbb{Z}} \left|\mathrm{sinc}\left(\frac{1}{2}(\omega + 2\pi k)\right)^L \right|^2 =
-\sin^{2L}\left(\frac{\omega}{2}\right) \frac{1}{(2L-1)!} \frac{d^{2L-1}}{d\left(\frac{\omega}{2}\right)^{2L-1}} \cot{\left(\frac{\omega}{2}\right)}

and with L = N+1:

\sum_{k \in \mathbb{Z}} |\Phi(\omega + 2\pi k)|^2 =
-\sin^{2(N+1)}\left(\frac{\omega}{2}\right) \frac{1}{(2N+1)!} \frac{d^{2N+1}}{d\left(\frac{\omega}{2}\right)^{2N+1}} \cot{\left(\frac{\omega}{2}\right)}

Therefore, together with \Phi(\omega) this yields:

\tilde{\Phi}(\omega) = \frac{(2N+1)!}{\left(\frac{\omega}{2}\right) \sin\left(\frac{\omega}{2}\right)^{N+1} \frac{d^{2N+1}}{d\left(\frac{\omega}{2}\right)^{2N+1}} \cot{\left(\frac{\omega}{2}\right)}}

and finally substituting for t(\omega):

f(\omega) = \frac{(2N+1)!}{\left(\frac{\omega}{2}\right) \sin\left(\frac{\omega}{2}\right)^{N+1} \frac{d^{2N+1}}{d\left(\frac{\omega}{2}\right)^{2N+1}} \cot{\left(\frac{\omega}{2}\right)}} e^{j \omega n}

As this function is not well defined it is better to use the limit:

c_{m,n} = j^m \lim_{\omega \rightarrow 0} f(\omega)


Examples for a cubic spline

For a cubic spline (N=3) the coefficients are:

c_{0,n}   & = & 1 \\
c_{1,n}   & = & n \\
c_{2,n}   & = & \frac{1}{3}\left( -1 + 3n^2 \right) \\
c_{3,n}   & = & -n + n^3

cubic spline reproducing polynomial of order 2
cubic spline reproducing polynomial of order 3



  1. P.L. Dragotti, M. Vetterli, T.Blu: "Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang-Fix", IEEE Transactions on Signal Processing, vol. 55, No. 5, May 2007
  3. S. Mallat: "A Wavelet Tour of Signal Processing", Academic Press 1999
  4. M.Unser: "Splines - A Perfect Fit for Signal and Imaging Processing", IEEE Signal Processing Magazine Nov. 1999
  5. M.J.C.S. Reis, P.J.S.G. Ferreira, S.F.S.P. Soares: "Linear combinations of B-splines as generating functions for signal approximation", Elsevier Digital Signal Processing 15, 2005
  6. L.V. Ahlfors: "Complex Analysis", McGraw-Hill, 1979
  7. I.J. Schoenberg: "Cardinal interpolation and spline functions", J. Approx. Theory volume 2, pp. 167-206, 1969


Manu said ...


--Manu 19:47, 19. Jul. 2010 (MSD)

Meine Werkzeuge