Since the 1970's B-splines have evolved to become the {em de facto}
standard for curve and surface representation due to many
of their salient properties. Conventional least-squares
scattered data fitting techniques for B-splines require the inversion
of potentially large matrices. This is time-consuming as well
as susceptible to ill-conditioning which leads to undesired
results. Lee {em et al.} proposed a novel B-spline
algorithm for fitting a 2-D cubic B-spline surface to scattered
data in cite{Lee}. The proposed algorithm utilizes an
optional multilevel approach for better fitting results.
We generalize this technique to support $N$-dimensional
data fitting as well as arbitrary degree of B-spline. In addition,
we generalize the B-spline kernel function class to accommodate
this new image filter.