# 2d interpolation method for coarsely sampled image

I'm looking for a general method for 2d interpolation of a coarsely sampled image. I'll use an example, taken from the scipy.interpolate (Python) page.

Say, I have this image, but instead of interpolating in the sense of going from e.g. a 512x512 grid to a 1024x1024 grid, I want to use the red/blue spots as data and re-define the unobserved green areas accordingly through an interpolation (so, the area between two red spots would be more yellow than green, and the area between two blue spots would be blue, not green). Of course, I'll do this programmatically. Language isn't too important, just looking for a method. I've tried kriging (available in IDL/Python), but I don't understand it enough to constrain the parameters, and for large (above ~64x64) arrays, it takes a lot of CPU/memory capacity.

Anyway, let me know if the question is too vague, and thanks...

Practically, I know that gstat supports anisotropic variogram models. You can use gstat as a stand alone program, although I greatly prefer the interface it has with R. See this tutorial of using gstat in R for more details, including an example of how to fit an anisotropic variogram model.
(Your example from that page is in false color -- try some other plt.imshow( cmap=plt.cm.xx ).)