Hi Zach, > Based on my reading of the two excellent Unser papers (both the one > that ndimage's spline code is based on, and the one that Travis > linked to), it seems like a major point of using splines for > interpolation is *better* behavior in the case of non-band-limited > data than the traditional 'anti-aliasing [e.g. lowpass] filter' + > 'resampling' + 'reconstruction [e.g. lowpass] filter' procedure.
It's certainly true that intermediate-order spline interpolants will cause less ringing than an "ideal" sinc function. So their behaviour is better for non-band-limited data than applying simplistic formulae derived from the Sampling Theorem. This fact would help you out if you don't use a low-pass filter. However, I wouldn't go as far as to say that splines *replace* some form of low-pass filtering. I haven't read Unser's papers in much detail, though (at least not recently) and my applications are different from yours; it may depend exactly what you're trying to do. > So it seems that something is wrong if the spline interpolation > tools in ndimage only work properly in the band-limited case, or > require lowpass filtering. It depends what you mean by working properly -- but in this case it does look like something is wrong and that you figured it out in your next post :-). James. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion