A highlevel approach to splines is to define them via some suitable generalized differential operator L. Specifically, we say that the continuously defined function s(x) is a cardinal Lspline if and only if
L{s(x)}=∑ a[k]δ(xk)
In essence, the operator L extracts the spline singularities (stream of Dirac impulses), while acting as an analogtodigital converter: transformation of s(x) into a discrete sequence of weights a[k]. The simplest example is the derivative operator which is associated to piecewiseconstant splines. The concept remains valid in higher dimensions and for arbitrary lattices.
Within this abstract framework, we have chosen to identify interesting classes of shiftinvariant operators based on some general invariance principles. In the case of scale invariance, we have shown that the corresponding class of operators in 1D reduces to a twoparameter family of fractional derivatives. These generalized derivatives lead to the definition of an extended family of fractional splines that has the interesting property of being closed under fractional differentation. If we move to higher dimensions and add rotation invariance, we end up with fractional iterates of the Laplacian, the defining operators of polyharmonic splines. A nice side effect of the scaleinvariance property is that the underlying spline functions satisfy scaling relations which makes them suitable for constructing wavelet bases.
Along the same line, we have shown that certain types of Box splines (on a hexagonal grid) could be associated with a special multidimensional iterated derivative operator. This formulation yields explicit formulas for the underlying Bsplines, which were not available previously.
The proposed spline formalism also lends itself to the specification of wavelet basis functions that essentially behave like multiscale versions of the underlying operator. We have demonstrated the concept with the exponentialspline wavelets, which can be tuned to replicate the behavior of any ordinary (constantcoefficient) differential operator.
We have also considered fractional iterates of the Laplacian which have led to the construction of new, nonseparable wavelets that are nearly isotropic, and act like smoothed versions of the Laplacian. 

[1]  I. Khalidov, J. Fadili, F. Lazeyras, D. Van De Ville, M. Unser, "Activelets: Wavelets for Sparse Representation of Hemodynamic Responses," Signal Processing, vol. 91, no. 12, pp. 28102821, December 2011.

[3]  P.D. Tafti, D. Van De Ville, M. Unser, "Invariances, LaplacianLike Wavelet Bases, and the Whitening of Fractal Processes," IEEE Transactions on Image Processing, vol. 18, no. 4, pp. 689702, April 2009.

[11]  I. Khalidov, T. Blu, M. Unser, "Generalized LSpline Wavelet Bases," Proceedings of the SPIE Optics and Photonics 2005 Conference on Mathematical Methods: Wavelet XI, San Diego CA, USA, July 31August 3, 2005, vol. 5914, pp. 59140F1/59140F8.

[12]  M. Unser, T. Blu, "Wavelet Theory Demystified," IEEE Transactions on Signal Processing, vol. 51, no. 2, pp. 470483, February 2003.

[13]  T. Blu, M. Unser, "A Complete Family of Scaling Functions: The (α, τ)Fractional Splines," Proceedings of the TwentyEighth IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03), Hong Kong SAR, People's Republic of China, April 610, 2003, vol. VI, pp. 421424.

