Since last time (Taylor Expansion & Cross Correlation,Coalignment Code), I have attempted to re-do the cross-correlation with an added component: error estimates. It turns out, there is a better method than the Taylor-expansion around the cross-correlation peak.  Fourier upsampling can be used to efficiently determine precise sub-pixel offsets (matlab version, Manuel Guizar, author, refereed article). However, in the published methods just cited, there is no way to determine the error - those algorithms are designed to measure offsets between identical images corrupted by noise but still strongly dominated by signal. We're more interested in the case where individual pixels may well be noise-dominated, but the overall signal in the map is still large. So, I've developed a python translation of the above codes and then some. Image Registration on github The docstrings are pretty solid, but there is no overall documentation. However, there's a pretty good demo of the simulation AND fitting code here: Tests and Examples The results for the Bolocam data are here (only applied to v2-Herschel offsets):