Phase Unwrapping as an ill-posed problem: performance comparison between a neural network based approach and a stochastic search method.

2-D phase unwrapping, a problem common to signal processing, optics, and interferometric radar topographic applications, consists in retrieving an absolute phase field from principal, noisy measurements. In this paper, we analyze the application of neural networks to this complex mathematical problem, formulating it as a learning-by-examples strategy, by training a multi layer perceptron to associate a proper correction pattern to the principal phase gradient configuration on a local window. In spite of the high dimensionality of this problem, the proposed MLP, trained on examples from simulated phase surfaces, shows to be able to correctly remove more than half the original number of pointlike inconsistencies on real noisy interferograms. Better efficiencies could be achieved by enlarging the processing window size, so as to exploit a greater amount of information. By pushing further this change of perspective, one passes from a local to a global point of view; problems of this kind are more effectively solved, rather than through learning strategies, by minimization procedures, for which we propose a powerful algorithm, based on a stochastic approach.