SO2 cameras are rapidly gaining popularity as a tool for monitoring SO2 emissions from volcanoes. Several different SO2 camera systems have been developed with varying patterns of image acquisition in space, time and wavelength. Despite this diversity, there are two steps common to the workflows of most of these systems; aligning images of different wavelengths to calculate apparent absorbance and estimating plume transport speeds, both of which can be achieved using motion estimation algorithms. Here we present two such algorithms, a Dual Tree Complex Wavelet Transform-based algorithm and the Farnebäck Optical Flow algorithm. We assess their accuracy using a synthetic dataset created using the numeric cloud-resolving model ATHAM, and then apply them to real world data from Villarrica volcano. Both algorithms are found to perform well and the ATHAM simulations offer useful datasets for benchmarking and validating future algorithms.