I have written a C version of the recursive Gaussian filter. It is more accurate than gaussian_filter in ndimage (less truncation error near edges, as shown before) and also faster. Here are some timings on filtering the "lenna" test image on my computer: sigma = 5 elapsed time (iir with openmp): 81.366640 ms elapsed time (iir): 107.282360 ms elapsed time (ndimage): 137.548760 ms sigma = 9 elapsed time (iir with openmp): 44.403760 ms elapsed time (iir): 75.285720 ms elapsed time (ndimage): 163.750920 ms sigma = 21 elapsed time (iir with openmp): 45.063040 ms elapsed time (iir): 75.941400 ms elapsed time (ndimage): 313.359080 ms sigma = 101 elapsed time (iir with openmp): 56.134120 ms elapsed time (iir): 87.622240 ms elapsed time (ndimage): 1210.016680 ms It is still only written for np.float64, but it would be easy to make optimized versions for various dtypes, including rgb images. You don't really see the scaling effect of OpenMP here, as the only thing that changes with sigma is the amount of zero-padding. Anyhow, this beats ndimage on speed and accuracy, and scales much better with sigma. It is not restricted to 2 dimensions. It can filter along any axis of an ndarray. Thus another use case is fast kernel density estimation in nd space. Regards, Sturla Molden