Abstract
We propose a novel layered complex-valued neural network to reduce singular points (SP’s) in phase images to obtain digital elevation maps (DEM’s) through phase unwrapping. First we prepare a SPfree distorted image for a wrapped image data by constructive Fourier synthesis. We patch fractions of the SP-free image at the SP locations of the raw image and, then, feed it to estimation layer of the network as the initial image. The estimation layer interacts with raw-image layer with a complex-valued neurodynamics to yield a better estimation in which the SP number is reduced. The proposal reduces the calculation cost of unwrapping process and also increases the accuracy of the output DEM.
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© 2003 Springer-Verlag Berlin Heidelberg
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Minami, M., Hirose, A. (2003). Phase Singular Points Reduction by a Layered Complex-Valued Neural Network in Combination with Constructive Fourier Synthesis. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_112
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DOI: https://doi.org/10.1007/3-540-44989-2_112
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