Re: Interpolation w/ cubic convolution kernel - boundary treatment?

On Apr 27, 2:00 am, Markus <iandj...@xxxxxxxxx> wrote:
My problem is not dsp related, but the method is closely related to
methods in dsp, so I am hoping to get some help here.

I am using the symmetric cubic convolution kernel ("Catmull-Rom
splines") to interpolate data over a limited range in a variable x.
For the interpolation I am using typically 10 nodes which are
equidistant in x.
Example: The interpolated function between nodes 4 and 5 is computed
based on the data points at nodes 3,4,5,6
A nice property of the Catmull-Rom splines is that I get a continuous
1st derivative everywhere.

sounds like Hermite polynomials.

My question is now: how should I treat the ranges near the boundary?
With 10 nodes, how should I interpolate the data between node 9 and
10? So far I am using linear interpolation here - but this is
conceptually ugly (and it's not precise, although the latter is not my
biggest problem since I want a _nice_ solution).
I am especially worried that in my current approach the interpolated
function has no continuous 1st derivative at node 9.

Is there a solution, in which I could use e.g. a non-symmetric
convolution kernel to interpolate between nodes 9 and 10, based on the
nodes 8,9,10 or maybe 7,8,9,10 - in a way that I get a continuous 1st
derivative everywhere?
I have not found any discussion about the boundary-treatment in the
literature (and neither on the Google-wide web). In image processing
sometimes people mirror the image at the boundaries (i.e. they would
introduce a hypothetical 11th node for which the data value is set
equal to the data at the 9th node and then interpolate using the data
points at nodes 8,9,10,11) - but this would not work in my case.

What I really want in the end, is the _kernel_ for the interpolation
at
the boundaries.

quadratic polynomial? now you have three constraints to satisfy
instead of four.

r b-j


.

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