  Interferometry can measure the shape or the material density of a system that
could not be measured otherwise by recording the difference between the phase
change of a signal and a reference phase. This difference is always between
$-\pi$ and $\pi$ while it is the absolute phase that is required to get a true
measurement. There is a long history of methods designed to recover accurately
this phase from the phase "wrapped" inside $]-\pi,\pi]$. However, noise and
under-sampling limit the effectiveness of most techniques and require highly
sophisticated algorithms that can process imperfect measurements. Ultimately,
analysing successfully an interferogram amounts to pattern recognition, a task
where radial basis function neural networks truly excel at. The proposed neural
network is designed to unwrap the phase from two-dimensional interferograms,
where aliasing, stemming from under-resolved regions, and noise levels are
significant. The neural network can be trained in parallel and in three stages,
using gradient-based supervised learning. Parallelism allows to handle
relatively large data sets, but requires a supplemental step to synchronized
the fully unwrapped phase across the different networks.
