PS... (sorry for all the posts, for anyone who isn't interested...)
Agreed, it looks like aliasing. Nevertheless, any resampling procedure is supposed to deal with this internally, right? Either by lowpass filtering (traditional case), or by spline fitting (spline case as described by Unser and understood by me) -- it shouldn't be letting aliasing bubble through, correct?
In the general case, I don't think it is appropriate for the resampling procedure to use low-pass filtering internally to avoid artefacts, except perhaps when downsampling. It probably makes sense for computer graphics work, but there are cases where the input data are band limited to begin with and any degradation in resolution is unacceptable. Where needed, I think low-pass filtering should either be the responsibility of the main program or an option. It's not even possible for the resampling procedure to prevent artefacts in every case, since the aliasing in a badly undersampled image cannot be removed post factum (this is for undersampled photos rather than artificial graphics, which I think are fundamentally different because everything is defined on the grid, although I haven't sat down and proved it mathematically). I'm also not sure how the procedure could decide on the level of smoothing needed for a given dataset without external information. Of course intermediate-order splines will probably keep everyone happy, being reasonably robust against ringing effects without causing much smoothing or interpolation error :-). By the way, I think you and Stefan might be interested in a medical imaging paper by Lehmann et al. (1999), which gives a very nice overview of the properties of different interpolation kernels: http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/42/17698/00816070.pdf?... For what it's worth, I'd agree with both of you that the numeric overflow should be documented if not fixed. It sounds like Stefan has figured out a solution for it though. If you make sense of the code in "ni_interpolation.c", Stefan, I'd be very interested in how to make it calculate one less value at the edges :-). Cheers, James.