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

Maria Teresa Chiaradia; Luciano Guerriero; Alberto Refice; Guido Pasquariello; Giuseppe Satalino; Sebastiano Stramaglia

doi:10.1117/12.326700

13 October 1998 Phase unwrapping as an ill-posed problem: performance comparison between a neural-network-based approach and a stochastic search method

Maria Teresa Chiaradia, Luciano Guerriero, Alberto Refice, Guido Pasquariello, Giuseppe Satalino, Sebastiano Stramaglia

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Proceedings Volume 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation; (1998) https://doi.org/10.1117/12.326700
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

2D 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 multilayer perceptron to associate a proper correction pattern to the principal phase gradient configuration on 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 prose a powerful algorithm, based on a stochastic approach.

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Maria Teresa Chiaradia, Luciano Guerriero, Alberto Refice, Guido Pasquariello, Giuseppe Satalino, and Sebastiano Stramaglia "Phase unwrapping as an ill-posed problem: performance comparison between a neural-network-based approach and a stochastic search method", Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); https://doi.org/10.1117/12.326700

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KEYWORDS

Neural networks

Stochastic processes

Interferometry

Avalanche photodetectors

Image processing

Neurons

Algorithms

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